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There has been an increased interest in characterizing and interpreting ecological heterogeneity over space and time in the past two decades. This is mainly due to the renewed recognition of the significance of heterogeneity in ecological theories. However, studies that have combined both spatial and temporal aspects of heterogeneity have been rare. A unified approach to define and quantify heterogeneity has also been lacking. Designed to overcome these problems, this study was conducted in a 40-year-old Pinus elliottii Engelm. forest at the Savannah River Site near Aiken, SC, USA with the following specific objectives: 1) to characterize the spatial patterns of soil and forest floor variables (moisture, pH, soil available nitrogen and phosphate, forest floor and soil carbon and nitrogen), 2) to examine the dynamics of these spatial patterns in response to two types of disturbance: whole-tree harvesting and girdling, and 3) to evaluate some of the current methods for quantifying ecological heterogeneity. In response to both disturbance treatments, spatial heterogeneity measured by sample variance showed a marked "increase and then decline" temporal pattern in soil moisture, soil available nitrogen and phosphorus. Similar patterns were not found in total soil C and N, and total litter C and N. Harvesting resulted in greater and more drastic changes in the variations of soil nutrients and water than did girdling. Despite the popularity of semivariogram analysis in recent ecological studies, the technique did not provide consistent results on patterns of heterogeneity in our system. A simulation experiment demonstrated that semivariogram analysis may suffer from many problems when it is used to characterize patchiness, one form of heterogeneity. The results from this study have a number of implications. First, spatial patterns of soil resources are high dynamic. The dynamics of patterns in soil resources may partly account for the weak correlation between vegetation and soil observed in ecological literature. Second, heterogeneity may be most effectively quantified by first identifying quantifiable components and then quantifying these components individually. A common pattern can be sought by comparing patterns of different components of heterogeneity for a given ecological property and by comparing patterns of different ecological variables for a given component of heterogeneity. Third, compared to surveys, field manipulative experiments can provide information that link patterns with ecological processes. As such, this study adds to ecological literature valuable information on temporal changes of soil heterogeneity following disturbance and conceptual advances in the quantification of ecological heterogeneity. / Ph. D.

In this thesis, we propose a statistical framework for estimating correlation between sensor systems measuring diverse physical phenomenon. We consider systems that measure at different temporal frequencies and measure responses with different dimensionalities. Our goal is to provide estimates of correlation between all pairs of sensors and use this information to flag potentially anomalous readings. Our anomaly detection method consists of two primary components: dimensionality reduction through projection and Gaussian process (GP) regression. We use non-metric multidimensional scaling to project a partially observed and potentially non-definite covariance matrix into a low dimensional manifold. The projection is estimated in such a way that positively correlated sensors are close to each other and negatively correlated sensors are distant. We then fit a Gaussian process given these positions and use it to make predictions at our observed locations. Because of the large amount of data we wish to consider, we develop methods to scale GP estimation by taking advantage of the replication structure in the data. Finally, we introduce a semi-supervised method to incorporate expert input into a GP model. We are able to learn a probability surface defined over locations and responses based on sets of points labeled by an analyst as either anomalous or nominal. This allows us to discount the influence of points resembling anomalies without removing them based on a threshold. / Ph. D.

Characterizing complex tissues requires precise identification of distinctive cell types, cell-specific signatures, and subpopulation proportions. Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in studying individual subpopulations and repopulation dynamics. Tissue heterogeneity cannot be resolved directly by most global molecular and genomic profiling methods. While signal deconvolution has widespread applications in many real-world problems, there are significant limitations associated with existing methods, mainly unrealistic assumptions and heuristics, leading to inaccurate or incorrect results. In this study, we formulate the signal deconvolution task as a blind source separation problem, and develop novel unsupervised deconvolution methods within the Convex Analysis of Mixtures (CAM) framework, for characterizing multi-scale tissue heterogeneity. We also explanatorily test the application of Significant Intercellular Genomic Heterogeneity (SIGH) method. Unlike existing deconvolution methods, CAM can identify tissue-specific markers directly from mixed signals, a critical task, without relying on any prior knowledge. Fundamental to the success of our approach is a geometric exploitation of tissue-specific markers and signal non-negativity. Using a well-grounded mathematical framework, we have proved new theorems showing that the scatter simplex of mixed signals is a rotated and compressed version of the scatter simplex of pure signals and that the resident markers at the vertices of the scatter simplex are the tissue-specific markers. The algorithm works by geometrically locating the vertices of the scatter simplex of measured signals and their resident markers. The minimum description length (MDL) criterion is applied to determine the number of tissue populations in the sample. Based on CAM principle, we integrated nonnegative independent component analysis (nICA) and convex matrix factorization (CMF) methods, developed CAM-nICA/CMF algorithm, and applied them to multiple gene expression, methylation and protein datasets, achieving very promising results validated by the ground truth or gene enrichment analysis. We integrated CAM with compartment modeling (CM) and developed multi-tissue compartment modeling (MTCM) algorithm, tested on real DCE-MRI data derived from mouse models with consistent and plausible results. We also developed an open-source R-Java software package that implements various CAM based algorithms, including an R package approved by Bioconductor specifically for tumor-stroma deconvolution. While intercellular heterogeneity is often manifested by multiple clones with distinct sequences, systematic efforts to characterize intercellular genomic heterogeneity must effectively distinguish significant genuine clonal sequences from probabilistic fake derivatives. Based on the preliminary studies originally targeting immune T-cells, we tested and applied the SIGH algorithm to characterize intercellular heterogeneity directly from mixed sequencing reads. SIGH works by exploiting the statistical differences in both the sequencing error rates at different nucleobases and the read counts of fake sequences in relation to genuine clones of variable abundance. / Ph. D.

The big data era has driven the needs for data analysis in every aspect of our daily lives. With the rapid growth of data size and complexity of data analysis models, modern big data analytic applications face the challenge to provide timely results often with limited resources. Such demand drives the growth of new hardware resources including GPUs and FPGAs, as well as storage devices such as SSDs and NVMs. It is challenging to manage the resources available in a cost restricted environment to best serve the applications with different characteristics. Extant approaches are agnostic to such heterogeneity in both underlying resources and workloads and require user knowledge and manual configuration for best performance. In this dissertation, we design, and implement a series of novel techniques, algorithms, and frameworks, to realize workload-aware resource management and scheduling. We demonstrate our techniques for efficient resource management across memory resource for in-memory data analytic platforms, processing resources for compute-intensive machine learning applications, and finally we design and develop a workload and heterogeneity-aware scheduler for general big data platforms. The dissertation demonstrates that designing an effective resource manager requires efforts from both application and system side. The presented approach makes and joins the efforts on both sides to provide a holistic heterogeneity-aware resource manage and scheduling system. We are able to avoid task failure due to resource unavailability by workload-aware resource management, and improve the performance of data processing frameworks by carefully scheduling tasks according to the task characteristics and utilization and availability of the resources. / Ph. D. / Clusters of multiple computers connected through internet are often deployed in industry for larger scale data processing or computation that cannot be handled by standalone computers. In such a cluster, resources such as CPU, memory, disks are integrated to work together. It is important to manage a pool of such resources in a cluster to efficiently work together to provide better performance for workloads running on top. This role is taken by a software component in the middle layer called resource manager. Resource manager coordinates the resources in the computers and schedule tasks to them for computation. This dissertation reveals that current resource managers often partition resources statically hence cannot capture the dynamic resource needs of workloads as well as the heterogeneous configurations of the underlying resources. For example, some computers in a clsuter might be older than the others with slower CPU, less memory, etc. Workloads can show different resource needs. Watching YouTube require a lot of network resource while playing games demands powerful GPUs. To this end, the disseration proposes novel approaches to manage resources that are able to capture the heterogeneity of resources and dynamic workload needs, based on which, it can achieve efficient resource management, and schedule the right task to the right resource.

Currently, the application of bioremediation requires extensive and costly monitoring due to limited understanding of the terminal electron accepting processes (TEAPs) that control biodegradation, which impairs the accurate quantification of contaminant mass loss. The measurement of redox conditions and evaluation of TEAPs are critical in assessing the capacity for bioremediation at any site. A series of batch microcosm experiments, using sediment collected from a gasoline-contaminated aquifer at Fort McCoy, Wisconsin, was designed to: 1) evaluate the role of Fe(III) in the development of TEAPs during biodegradation of benzene, toluene, ethylbenzene, and the xylenes (BTEX); 2) examine the biodegradation potential in different portions of the plume; and 3) compare methods of TEAP characterization. In general, the presence of Fe-oxides in microcosms inhibited methanogenesis. Although Fe-reducers did not actively degrade BTEX, Fe-reduction did occur, and most probable number (MPN) counts showed that added Fe(III) increased numbers of Fe-reducers in the microcosms. Methane production in microcosms constructed from sediment near the source area was ~5 times lower than levels produced by the mid-plume sediment. No Fe-reduction occurred in microcosms containing sediment from the source area. These results suggest that the source area has much lower biological activity than the mid-plume. TEAP characterization was conducted using a variety of methods, including geochemical indicators, redox dyes, MPN, and hydrogen concentrations. Monitoring of CH4 concentration yielded useful information in delineation of redox processes; Fe(II) monitoring was unreliable as a geochemical indicator. Redox dyes supplied basic information on reducing environments. MPN counts estimated microbial populations in lieu of faulty geochemical indicators, i.e., Fe(II). The measurement of H2 proved to be one of the more simple and reliable methods for TEAP identification. Results of this study indicate that TEAP characterization should include use of multiple methods; relying on geochemical indicators alone is not sufficient. / Master of Science

O objetivo desta tese é identificar a contribuição dos efeitos de aglomeração e do sorting espacial, associado às heterogeneidades não observadas dos trabalhadores e firmas, para a determinação dos salários individuais e dos salários locais nos arranjos populacionais do Brasil. Os dados do mercado de trabalho (RAIS-MTE) mostram que existe um diferencial espacial de salários, o qual pode ser explicado pelas distintas composições produtivas e de trabalhadores entre os locais e pelos diferenciais de custo de vida. A disponibilidade crescente de micro dados longitudinais permitiu a inclusão das habilidades não observadas individuais na equação de salários. Os estudos da Economia do Trabalho mostram que as habilidades são responsáveis por uma grande parcela dos diferenciais de salários. No entanto, os estudos nacionais ainda encontram um diferencial significativo, mesmo após o controle dos componentes individuais e do custo de vida, sugerindo a existência de efeitos específicos associados à localização das firmas e dos trabalhadores. A Economia Urbana considera as economias de aglomeração como um determinante salarial relevante nos mercados de trabalhos densos, particularmente a partir dos trabalhos de Glaeser e Maré (1994; 2001). Por sua vez, a maior produtividade das áreas densas pode ser atribuída à concentração de trabalhadores e firmas mais produtivos, o que ficou conhecido nessa literatura como sorting. Os estudos da Economia Urbana controlam somente o sorting dos atributos individuais não observados. Este trabalho contribui com a literatura ao considerar o sorting espacial dos atributos não observados das firmas e dos trabalhadores na determinação dos salários e dos efeitos de aglomeração. O estudo utiliza um modelo de decomposição salarial para lidar com múltiplos efeitos fixos no painel pareado de trabalhadores e firmas. Os efeitos puros da aglomeração (densidade) sobre os salários locais serão estimados em um modelo de dois estágios. O primeiro estágio estima uma equação salarial incluindo as características observadas dos trabalhadores e do emprego e os efeitos de localização, com um painel de micro dados da RAIS (2002-2014). O segundo estágio realiza a decomposição dos efeitos de localização em componentes associados às características locais dos arranjos e aos atributos não observados das firmas e dos trabalhadores. A estratégia de identificação propõe o controle dos efeitos fixos dos trabalhadores e firmas e o uso de variável instrumental para identificar os efeitos da aglomeração. Ademais, os dados de satélite sobre a luminosidade noturna são usados para estimar a proporção da área total dos arranjos habitada, a qual é utilizada para calcular a densidade. Os resultados mostraram que os efeitos do trabalhador foram mais relevantes do que os efeitos da firma para explicar a variação dos salários individuais e locais. O modelo principal, que utiliza o instrumento Bartik e a área iluminada, encontrou um efeito da densidade sobre os salários locais de 4,9%, o qual é superior ao lower bound da literatura prévia (3%). Os resultados sugerem que ignorar as limitações indicadas neste estudo pode levar a uma subestimação nas estimativas dos efeitos da densidade / The goal of this study is to identify the contribution of agglomeration effects and spatial sorting for the determination of individual and local wages in Brazilian urban agglomerations. Administrative records from the Ministry of Labor (RAIS-MTE) show a spatial differential in wages, which can be explained by the different productive structures and compositions of workers across cities, and by differentials in cost-of-living. The longitudinal microdata allowed the inclusion of unobserved individual skills in the wage equation. Studies in Labor Economics show that skills are responsible for a large portion of the wage differential. However, the available studies on Brazil still find a significant differential, even after controlling for the individual components and the cost of living. This suggests the existence of specific effects associated with the location of firms and workers. The literature on Urban Economics considers the economies of agglomeration as a relevant wage determinant in dense labor markets. The higher productivity of dense areas can be attributed to the concentration of more productive workers and firms more productive, which became known in this literature as sorting. Studies in Urban Economics only control the sorting of unobserved individual attributes. This dissertation contributes to the literature by considering the spatial sorting of unobserved attributes of firms and of workers in the determination of wages and of the effects of agglomeration. The study uses a wage decomposition model to deal with multiple fixed effects in a matched panel of workers and firms. The pure effects of agglomeration (density) on local wages are estimated in a two-stage model. The first stage estimates a wage equation including the observed characteristics of workers and firms and the effects of location, with a microdata panel of RAIS (2002-2014). The second stage decomposes the location effects into components associated to local characteristics and to unobserved attributes of firms and workers. The identification strategy involves controlling for fixed effects of workers and firms, and using an instrumental variable to identifying the effects of agglomeration. Satellite data on illumination are used to estimate the proportion of the overall area occupied with population and firms in each local labour markets. The results indicate that the worker effects are more relevant to explain wage variation than the firm\'s effects. The model of preference indicates a density effect on wages of 4.9%, much higher than the literature lower bound (3%). This suggests that ignoring the variables included in this study can lead to an underestimation of the effects of agglomeration

O objetivo desta tese é identificar a contribuição dos efeitos de aglomeração e do sorting espacial, associado às heterogeneidades não observadas dos trabalhadores e firmas, para a determinação dos salários individuais e dos salários locais nos arranjos populacionais do Brasil. Os dados do mercado de trabalho (RAIS-MTE) mostram que existe um diferencial espacial de salários, o qual pode ser explicado pelas distintas composições produtivas e de trabalhadores entre os locais e pelos diferenciais de custo de vida. A disponibilidade crescente de micro dados longitudinais permitiu a inclusão das habilidades não observadas individuais na equação de salários. Os estudos da Economia do Trabalho mostram que as habilidades são responsáveis por uma grande parcela dos diferenciais de salários. No entanto, os estudos nacionais ainda encontram um diferencial significativo, mesmo após o controle dos componentes individuais e do custo de vida, sugerindo a existência de efeitos específicos associados à localização das firmas e dos trabalhadores. A Economia Urbana considera as economias de aglomeração como um determinante salarial relevante nos mercados de trabalhos densos, particularmente a partir dos trabalhos de Glaeser e Maré (1994; 2001). Por sua vez, a maior produtividade das áreas densas pode ser atribuída à concentração de trabalhadores e firmas mais produtivos, o que ficou conhecido nessa literatura como sorting. Os estudos da Economia Urbana controlam somente o sorting dos atributos individuais não observados. Este trabalho contribui com a literatura ao considerar o sorting espacial dos atributos não observados das firmas e dos trabalhadores na determinação dos salários e dos efeitos de aglomeração. O estudo utiliza um modelo de decomposição salarial para lidar com múltiplos efeitos fixos no painel pareado de trabalhadores e firmas. Os efeitos puros da aglomeração (densidade) sobre os salários locais serão estimados em um modelo de dois estágios. O primeiro estágio estima uma equação salarial incluindo as características observadas dos trabalhadores e do emprego e os efeitos de localização, com um painel de micro dados da RAIS (2002-2014). O segundo estágio realiza a decomposição dos efeitos de localização em componentes associados às características locais dos arranjos e aos atributos não observados das firmas e dos trabalhadores. A estratégia de identificação propõe o controle dos efeitos fixos dos trabalhadores e firmas e o uso de variável instrumental para identificar os efeitos da aglomeração. Ademais, os dados de satélite sobre a luminosidade noturna são usados para estimar a proporção da área total dos arranjos habitada, a qual é utilizada para calcular a densidade. Os resultados mostraram que os efeitos do trabalhador foram mais relevantes do que os efeitos da firma para explicar a variação dos salários individuais e locais. O modelo principal, que utiliza o instrumento Bartik e a área iluminada, encontrou um efeito da densidade sobre os salários locais de 4,9%, o qual é superior ao lower bound da literatura prévia (3%). Os resultados sugerem que ignorar as limitações indicadas neste estudo pode levar a uma subestimação nas estimativas dos efeitos da densidade / The goal of this study is to identify the contribution of agglomeration effects and spatial sorting for the determination of individual and local wages in Brazilian urban agglomerations. Administrative records from the Ministry of Labor (RAIS-MTE) show a spatial differential in wages, which can be explained by the different productive structures and compositions of workers across cities, and by differentials in cost-of-living. The longitudinal microdata allowed the inclusion of unobserved individual skills in the wage equation. Studies in Labor Economics show that skills are responsible for a large portion of the wage differential. However, the available studies on Brazil still find a significant differential, even after controlling for the individual components and the cost of living. This suggests the existence of specific effects associated with the location of firms and workers. The literature on Urban Economics considers the economies of agglomeration as a relevant wage determinant in dense labor markets. The higher productivity of dense areas can be attributed to the concentration of more productive workers and firms more productive, which became known in this literature as sorting. Studies in Urban Economics only control the sorting of unobserved individual attributes. This dissertation contributes to the literature by considering the spatial sorting of unobserved attributes of firms and of workers in the determination of wages and of the effects of agglomeration. The study uses a wage decomposition model to deal with multiple fixed effects in a matched panel of workers and firms. The pure effects of agglomeration (density) on local wages are estimated in a two-stage model. The first stage estimates a wage equation including the observed characteristics of workers and firms and the effects of location, with a microdata panel of RAIS (2002-2014). The second stage decomposes the location effects into components associated to local characteristics and to unobserved attributes of firms and workers. The identification strategy involves controlling for fixed effects of workers and firms, and using an instrumental variable to identifying the effects of agglomeration. Satellite data on illumination are used to estimate the proportion of the overall area occupied with population and firms in each local labour markets. The results indicate that the worker effects are more relevant to explain wage variation than the firm\'s effects. The model of preference indicates a density effect on wages of 4.9%, much higher than the literature lower bound (3%). This suggests that ignoring the variables included in this study can lead to an underestimation of the effects of agglomeration

Up to 60% of the World’s oil is now within carbonates, with over 50% in the Middle East. Many existing carbonate fields have very low oil recoveries due to multiple scales of pore heterogeneity. To secure better recoveries the controls from deposition and diagenesis towards the origin of carbonate pore heterogeneity needs better understanding. To provide good sample support, three High frequency Cycle’s were sampled (2 from the Lekhwair Formation and the third being the Lower Kharaib Formation) from an offshore field (Abu Dhabi) along a southwest-northeast transect, encompassing the oil leg, transition zone, water leg, the field crest and two opposing flanks. With respect to deposition, the 4th order Sequence Boundaries’ (hardgrounds) and the Maximum Flooding Surface’s were correlated across the field, within the sequence stratigraphic framework, showing that each HFC, of the Lekhwair Formation, contains laterally continuous reservoirs (4th order HST’s) which are compartmentalised above and below by impermeable seals (4th order TST’s). The Lower Kharaib Formation shows significant shoaling producing the shallowest platform (prolonged 3rd order TST) and the best connected reservoir facies. With respect to diagenesis, δ 18O isotopes trends, from calcite cement zones within macrocements from the water and oil legs, in comparison with oil inclusion abundances suggest that oil charge reduced cementation in the crest macropores. Stylolitisation in the water leg at deep burial provided solutes for new cement nucleation causing near complete macropore occlusion. The most open micropore networks coincide with the highest porosity/permeability relationships at the mid-late HST’s of each HFC. Considering these areas could be lower grade reservoirs, and that pore characterisation by Lucia (1999) does not include identifying and quantifying micropores, a new ‘Micropore model’ (using elements from the Petrotype atlas method) is devised. This new method highlights micropore-dominated areas alongside macropore-dominated areas within specific reservoir horizons. This provides information of pore heterogeneity at several scales within a carbonate reservoir and may determine the method for oil extraction and increase oil recovery from both the Lekhwair and Lower Kharaib Formations.

Mycobacterial populations are known for the heterogeneity in terms of cell size, morphology, and metabolic status, which are believed to help the population survive under stress conditions. Such population heterogeneity had been observed in TB patients, in animal models, and in in vitro cultures. Also, the physiological relevance of population heterogeneity under nutrient starvation has been studied. However, the physiological significance of population heterogeneity in oxidative and nitrite stress has not been addressed yet. Our laboratory had earlier shown that a subpopulation of mycobacterial mid-log phase cultures divide by highly deviated asymmetric division, generating short cells and normal-sized/long cells. This proportion has been found to be consistent and reproducible, and has been found in the freshly diagnosed pulmonary tuberculosis patients’ sputum, which is known to have high levels of oxidative stress. The highly deviated asymmetric cell division has been found to be one of the mechanisms that mycobacteria use to generate cell size heterogeneity in the population. However, the physiological significance of the population heterogeneity generated by the highly deviated asymmetric division remained to be addressed. Therefore, in the present study, we addressed the physiological significance of the generation of population heterogeneity in terms of cell size in Mycobacterium smegmatis and Mycobacterium tuberculosis. In this regard, we explored whether the minor subpopulation of short cells generated in the population has any relevance in the response of mycobacteria to oxidative and nitrite stress for survival. The Chapter 1, which forms the Introduction to the thesis, gives an extensive literature survey on the phenotypic heterogeneity in diverse bacterial systems and the physiological significance of such heterogeneity. Subsequently, an account of the phenotypic heterogeneity reported in mycobacteria is given, with examples of its significance implicated for survival under nutrient stress. Then an account of various studies on the oxidative and nitrite stress response of mycobacteria and on the genes involved in those processes are given. Further, the present study is justified by stating that so far there has not been any study to find out the physiological relevance of phenotypic heterogeneity on oxidative and nitrite stress response in mycobacteria. Finally, the Introduction is concluded by stating that the present study investigates and reports for the first time the physiological significance of the minor subpopulation of short cells for survival under oxidative and nitrite stress conditions. The Chapter 2 forms the Materials and Methods used in the present study. Here a detailed description of the methods used for the separation of the short cells, their characterisation, stress response, and so on are given in great detail. The Chapter 3 forms the first data chapter that presents results on the nature of response of Mycobacterium smegmatis and Mycobacterium tuberculosis against oxidative and nitrite stress. Here the cell size natural distribution, in terms of short cells and normal-sized/long cells in the mid-log phase population, the fractionation and enrichment of these subpopulations, differential susceptibility of the cells in the fractions to the stress conditions, the enhanced survival of the population upon mixing of these cell populations at the natural proportion, and the decreased survival upon mixing them at unnatural proportion are presented. The differential survival of the short cells and normal-sized/long cells was studied at a variety of stress concentrations for oxidative (H2O2) and nitrite (acidified sodium nitrite, pH 5), cell densities and exposure time to show the robustness of the phenomenon. Enhanced survival upon extended exposure to stress also has been documented. Essentially the data in this chapter shows that although the different sized populations show differential stress susceptibility to the stress conditions, their combined presence at the proportion that naturally exists in the mid-log phase population enhances the survival of the population, at the cost of the highly susceptible short cells for the enhanced survival of the less susceptible normal-sized/long cells, kin selection and altruism. The Chapter concludes with a discussion on the results. The Chapter 4 delineates the mechanism of the altruistic phenomenon that results in the enhanced survival of the population at the sacrifice of the minor subpopulation of short cells. Here we present evidence that hydroxyl radical generated through Fenton reaction is responsible for the enhanced survival through the induction of the synthesis of catalase-peroxidase (KatG) for the degradation of H2O2. The free iron deficient short cells acquire more iron, which in turn becomes stoichiometrically detrimental to them due to the high levels of hydroxyl generation in the presence of H2O2. On the contrary, the free iron containing normal-sized/long cells do not acquire iron and hence the hydroxyl radical produced in the population becomes stoichiometrically beneficial to them. Thus, the deficiency of free iron which consequentially necessitates the short cells to acquire more iron becomes a maladaptive trait in the presence of H2O2 but gets co-opted in kin selection, for the survival of the normal-sized/long cells that form major proportion of the population – a phenomenon reminiscent of altruism. The Chapter concludes with a model depicting the entire phenomenon and a discussion on the results and their implications.