The effects of immortalization on the N-glycome and proteome of CDK4-transformed lung cancer cells.

Biological experiments are often conducted in vitro using immortalized cells due to their accessibility and ease of propagation compared to primary cells and live animals. However, immortalized cells may present different proteomic and glycoproteomic characteristics from the primary cell source due to the introduction of genes that enhance proliferation (e.g. CDK4) or enable telomere lengthening. To demonstrate the changes in phenotype upon CDK4-transformation, we performed LC-MS/MS glycomic and proteomic characterizations of a human lung cancer primary cell line (DTW75) and a CDK4-transformed cell line (GL01) derived from DTW75. We observed that the primary and CDK4-transformed cells expressed significantly different levels of sialylated, fucosylated, and sialofucosylated N-glycans. Specifically, the primary cells expressed higher levels of hybrid- and complex-type sialylated N-glycans, while CDK4-transformed cells expressed higher levels of complex-type fucosylated and sialofucosylated N-glycans. Further, we compared the proteomic differences between the cell lines and found that CDK4-transformed cells expressed higher levels of RNA-binding and adhesion proteins. Further, we observed that the CDK4-transformed cells changed N-glycosylation after 31 days in cell culture, with a decrease in high-mannose and increase in fucosylated, sialylated, and sialofucosylated N-glycans. Identifying these changes between primary and CDK4-transformed cells will provide useful insight when adapting cell lines that more closely resemble in vivo physiological conditions.

Measuring the unmeasurable multidimensional poverty for economic development: Datasets, algorithms, and models from the poorest region of Luzon, Philippines.

Poverty is the oldest social problem that ever existed and is difficult to reverse. It is multidimensional and unmeasurable. Thus, measuring by decomposing rural multidimensional poverty is critical. Most poverty studies are usually generic, exposed to large sampling errors, and intended for macroeconomic decisions. Thus, measuring poverty for a specific locality with various configurations is crucial for economic development. This work presents a processed and analyzed dataset from a huge community-based monitoring system of Goa, Camarines Sur. The local is situated in the poorest district, of the poorest province, in the poorest region of Luzon, Philippines. Research about poverty in this area is limited and measuring poverty at specific locality is scarce. The datasets contain the multidimensional poverty indicators, health, and nutrition, housing and settlement, water and sanitation, basic education from elementary to senior high school, income classifications, employment and livelihood, peace and order, summary of calamity occurrences experienced by residents, disaster risk reduction preparedness, figures of diagnostic analytics, tables of descriptive analytics, poverty analytics, measurement of decomposed poverty, summary of disaggregated configurations, graphs of predictive and prescriptive analytics, and population dynamics. This work is vital in analyzing poverty in rural and multidimensional approaches through poverty incidence, poverty gap, severity statistics, watts index, and classifications. It may also serve as a basis for measuring poverty from nearby regions and nations that use complete enumeration of its households and members. By utilizing the analyzed and processed data, further classifications and regressions can be done. It can be freely used by the government, private organizations, charitable institutions, businesses, academia, and researchers to target policies. An advantage of utilizing the dataset is to address multifaceted poverty that requires different interventions. It will facilitate the creation of programs to alleviate poverty and promote local economic development.

Integrating Computational Methods in Network Pharmacology and In Silico Screening to Uncover Multi-targeting Phytochemicals against Aberrant Protein Glycosylation in Lung Cancer.

Glycoproteins are an underexploited drug target for cancer therapeutics. In this work, we integrated computational methods in network pharmacology and in silico docking approaches to identify phytochemical compounds that could potentially interact with several cancer-associated glycoproteins. We first created a database of phytochemicals from selected plant species, Manilkara zapota (sapodilla/chico), Mangifera indica (mango), Annona muricata (soursop/guyabano), Artocarpus heterophyllus (jackfruit/langka), Lansium domesticum (langsat/lanzones), and Antidesma bunius (bignay), and performed pharmacokinetic analysis to determine their drug-likeness properties. We then constructed a phytochemical-glycoprotein interaction network and characterized the degree of interactions between the phytochemical compounds and with cancer-associated glycoproteins and other glycosylation-related proteins. We found a high degree of interactions from α-pinene (Mangifera indica), cyanomaclurin (Artocarpus heterophyllus), genistein (Annona muricata), kaempferol (Annona muricata and Antidesma bunius), norartocarpetin (Artocarpus heterophyllus), quercetin (Annona muricata, Antidesma bunius, Manilkara zapota, Mangifera indica), rutin (Annona muricata, Antidesma bunius, Lansium domesticum), and ellagic acid (Antidesma bunius and Mangifera indica). Subsequent docking analysis confirmed that these compounds could potentially bind to EGFR, AKT1, KDR, MMP2, MMP9, ERBB2, IGF1R, MTOR, and HRAS proteins, which are known cancer biomarkers. In vitro cytotoxicity assays of the plant extracts showed that the n-hexane, ethyl acetate, and methanol leaf extracts from A. muricata, L. domesticum and M. indica gave the highest growth inhibitory activity against A549 lung cancer cells. These may help further explain the reported cytotoxic activities of select compounds from these plant species.

In silico screening-based discovery of inhibitors against glycosylation proteins dysregulated in cancer.

Targeting enzymes associated with the biosynthesis of aberrant glycans is an under-utilized strategy in discovering potential inhibitors or drugs against cancer. The formation of cancer-associated glycans is mainly due to the dysregulated expression of glycosyltransferases and glycosidases, which play crucial roles in maintaining cellular structure and function. We screened a database of more than 14,000 compounds consisting of natural products and drugs for inhibition against four glycosylation enzymes - Alpha1-6FucT, ST6Gal1, ERMan1, and GlcNAcT-V. The top inhibitors identified against each enzyme were subsequently analyzed for potential binding against all four enzymes. In silico screening results show several promising candidates that could potentially inhibit all four enzymes: (1) Amb20622156 (demethylwedelolactone) [ERMan1: -9.3 kcal/mol; Alpha1-6FucT: -7.3 kcal/mol; ST6Gal1: -8.4 kcal/mol; GlcNAcT-V: -7.2 kcal/mol], (2) Amb22173588 (1,2-dihydrotanshinone I) [ERMan1: -9.3 kcal/mol; Alpha1-6FucT: -6.1 kcal/mol; ST6Gal1: -9.2 kcal/mol; GlcNAcT-V: -7.9 kcal/mol], and (3) Amb22173591 (tanshinol B) [ERMan1: -9.3 kcal/mol; Alpha1-6FucT: -6.0 kcal/mol; ST6Gal1: -9.8 kcal/mol; GlcNAcT-V: -7.7 kcal/mol]. Drug-enzyme active site residue interaction analyses show that the putative inhibitors form non-covalent bonding interactions with key active site residues in each enzyme, suggesting critical target residues in the four enzymes' active sites. Furthermore, pharmacokinetic property prediction analysis using pkCSM indicates that all of these inhibitors have good ADMETox properties (i.e., log P < 5, Caco-2 permeability > 0.90, intestinal absorption > 30%, skin permeability>-2.5, CNS permeability <-3, maximum tolerated dose < 0.477, minnow toxicity<-0.3). The in silico docking approach to glycosylation enzyme inhibitor prediction could help guide and streamline the discovery of novel inhibitors against enzymes involved in aberrant protein glycosylation.Communicated by Ramaswamy H. Sarma.

An Integrated Mass Spectrometry-Based Glycomics-Driven Glycoproteomics Analytical Platform to Functionally Characterize Glycosylation Inhibitors.

Cancer progression is linked to aberrant protein glycosylation due to the overexpression of several glycosylation enzymes. These enzymes are underexploited as potential anticancer drug targets and the development of rapid-screening methods and identification of glycosylation inhibitors are highly sought. An integrated bioinformatics and mass spectrometry-based glycomics-driven glycoproteomics analysis pipeline was performed to identify an N-glycan inhibitor against lung cancer cells. Combined network pharmacology and in silico screening approaches were used to identify a potential inhibitor, pictilisib, against several glycosylation-related proteins, such as Alpha1-6FucT, GlcNAcT-V, and Alpha2,6-ST-I. A glycomics assay of lung cancer cells treated with pictilisib showed a significant reduction in the fucosylation and sialylation of N-glycans, with an increase in high mannose-type glycans. Proteomics analysis and in vitro assays also showed significant upregulation of the proteins involved in apoptosis and cell adhesion, and the downregulation of proteins involved in cell cycle regulation, mRNA processing, and protein translation. Site-specific glycoproteomics analysis further showed that glycoproteins with reduced fucosylation and sialylation were involved in apoptosis, cell adhesion, DNA damage repair, and chemical response processes. To determine how the alterations in N-glycosylation impact glycoprotein dynamics, modeling of changes in glycan interactions of the ITGA5-ITGB1 (Integrin alpha 5-Integrin beta-1) complex revealed specific glycosites at the interface of these proteins that, when highly fucosylated and sialylated, such as in untreated A549 cells, form greater hydrogen bonding interactions compared to the high mannose-types in pictilisib-treated A549 cells. This study highlights the use of mass spectrometry to identify a potential glycosylation inhibitor and assessment of its impact on cell surface glycoprotein abundance and protein-protein interaction.

Optimal strategies and cost-benefit analysis of the [Formula: see text]-player weightlifting game.

The study of cooperation has been extensively studied in game theory. Especially, two-player two-strategy games have been categorized according to their equilibrium strategies and fully analysed. Recently, a grand unified game covering all types of two-player two-strategy games, i.e., the weightlifting game, was proposed. In the present study, we extend this two-player weightlifting game into an [Formula: see text]-player game. We investigate the conditions for pure strategy Nash equilibria and for Pareto optimal strategies, expressed in terms of the success probability and benefit-to-cost ratio of the weightlifting game. We also present a general characterization of [Formula: see text]-player games in terms of the proposed game. In terms of a concrete example, we present diagrams showing how the game category varies depending on the benefit-to-cost ratio. As a general rule, cooperation becomes difficult to achieve as group size increases because the success probability of weightlifting saturates towards unity. The present study provides insights into achieving behavioural cooperation in a large group by means of a cost-benefit analysis.

Local government responses for COVID-19 management in the Philippines.

BACKGROUND: Responses of subnational government units are crucial in the containment of the spread of pathogens in a country. To mitigate the impact of the COVID-19 pandemic, the Philippine national government through its Inter-Agency Task Force on Emerging Infectious Diseases outlined different quarantine measures wherein each level has a corresponding degree of rigidity from keeping only the essential businesses open to allowing all establishments to operate at a certain capacity. Other measures also involve prohibiting individuals at a certain age bracket from going outside of their homes. The local government units (LGUs)-municipalities and provinces-can adopt any of these measures depending on the extent of the pandemic in their locality. The purpose is to keep the number of infections and mortality at bay while minimizing the economic impact of the pandemic. Some LGUs have demonstrated a remarkable response to the COVID-19 pandemic. The purpose of this study is to identify notable non-pharmaceutical interventions of these outlying LGUs in the country using quantitative methods. METHODS: Data were taken from public databases such as Philippine Department of Health, Philippine Statistics Authority Census, and Google Community Mobility Reports. These are normalized using Z-transform. For each locality, infection and mortality data (dataset Y) were compared to the economic, health, and demographic data (dataset X) using Euclidean metric d=(x-y)2, where x∈X and y∈Y. If a data pair (x,y) exceeds, by two standard deviations, the mean of the Euclidean metric values between the sets X and Y, the pair is assumed to be a 'good' outlier. RESULTS: Our results showed that cluster of cities and provinces in Central Luzon (Region III), CALABARZON (Region IV-A), the National Capital Region (NCR), and Central Visayas (Region VII) are the 'good' outliers with respect to factors such as working population, population density, ICU beds, doctors on quarantine, number of frontliners and gross regional domestic product. Among metropolitan cities, Davao was a 'good' outlier with respect to demographic factors. CONCLUSIONS: Strict border control, early implementation of lockdowns, establishment of quarantine facilities, effective communication to the public, and monitoring efforts were the defining factors that helped these LGUs curtail the harm that was brought by the pandemic. If these policies are to be standardized, it would help any country's preparedness for future health emergencies.

Exaggerated evolution of male armaments via male-male competition.

Males usually compete to gain access to prospective mates. Through this male-male competition, superior males have a higher chance of passing on their traits to the next generation of male offspring. One category of male traits is armaments, which are weapons used during competition, for example, the chelae of fiddler crabs and the antlers of deer. One consequence of intrasexual selection is the exaggerated evolution of armaments, which can be limited by trade-offs, such as trade-offs with male body size. Here, we formulate a game-theoretic sexual selection model to explore the exaggerated evolution of armaments through male-male competition. The model is used to determine how competition affects the evolution of an armament that is subject to trade-offs. Our simulation can be used to support the exaggerated evolution hypothesis, that is, male-male competition escalates the rate of evolution of armaments.

Using Constrained Optimization for the Allocation of COVID-19 Vaccines in the Philippines.

BACKGROUND: Vaccine allocation is a national concern especially for countries such as the Philippines that have limited resources in acquiring COVID-19 vaccines. As such, certain groups are suggested to be prioritized for vaccination to protect the most vulnerable before vaccinating others. OBJECTIVE: The study aims to determine an optimal and equitable allocation of COVID-19 vaccines in the Philippines that will minimize the projected number of additional COVID-19 deaths while satisfying the priority groups for immediate vaccination. METHODS: In this study, a linear programming model is formulated to determine an allocation of vaccines such that COVID-19 deaths are minimized while the prioritization framework set by the government is satisfied. Data used were collected up to November 2020. Total vaccine supply, vaccine effectiveness, vaccine cost, and projected deaths are analyzed. Results of the model are also compared to other allocation approaches. RESULTS: Results of the model show that a vaccine coverage of around 60-70% of the population can be enough for a community with limited supplies, and an increase in vaccine supply is beneficial if the initial coverage is less than the specified target range. Additionally, among the vaccines considered in the study, the one with 89.9% effectiveness and a 183 Philippine peso price per dose projected the lowest number of deaths. Compared with other model variations and common allocation approaches, the model has achieved both an optimal and equitable allocation. CONCLUSIONS: Having a 100% coverage for vaccination with a 100% effectiveness rate of vaccine is ideal for all countries. However, some countries have limited resources. Therefore, the results of our study can be used by policymakers to determine an optimal and equitable distribution of COVID-19 vaccines for a country/community.

Improving environment drives dynamical change in social game structure.

The development of cooperation in human societies is a major unsolved problem in biological and social sciences. Extensive studies in game theory have shown that cooperative behaviour can evolve only under very limited conditions or with additional complexities, such as spatial structure. Non-trivial two-person games are categorized into three types of games, namely, the prisoner's dilemma game, the chicken game and the stag hunt game. Recently, the weight-lifting game has been shown to cover all five games depending on the success probability of weight lifting, which include the above three games and two trivial cases (all cooperation and all defection; conventionally not distinguished as separate classes). Here, we introduce the concept of the environmental value of a society. Cultural development and deterioration are represented by changes in this probability. We discuss cultural evolution in human societies and the biological communities of living systems.

A mathematical model of COVID-19 transmission between frontliners and the general public.

The number of COVID-19 cases is continuously increasing in different countries including the Philippines. It is estimated that the basic reproduction number of COVID-19 is around 1.5-4 (as of May 2020). The basic reproduction number characterizes the average number of persons that a primary case can directly infect in a population full of susceptible individuals. However, there can be superspreaders that can infect more than this estimated basic reproduction number. In this study, we formulate a conceptual mathematical model on the transmission dynamics of COVID-19 between the frontliners and the general public. We assume that the general public has a reproduction number between 1.5 and 4, and frontliners (e.g. healthcare workers, customer service and retail personnel, food service crews, and transport or delivery workers) have a higher reproduction number. Our simulations show that both the frontliners and the general public should be protected against the disease. Protecting only the frontliners will not result in flattening the epidemic curve. Protecting only the general public may flatten the epidemic curve but the infection risk faced by the frontliners is still high, which may eventually affect their work. The insights from our model remind us of the importance of community effort in controlling the transmission of the disease.

Optimal Allocation of COVID-19 Test Kits Among Accredited Testing Centers in the Philippines.

Testing is crucial for early detection, isolation, and treatment of coronavirus disease (COVID-19)-infected individuals. However, in resource-constrained countries such as the Philippines, test kits have limited availability. As of 11 April 2020, there are 11 testing centers in the country that have been accredited by the Department of Health (DOH) to conduct testing. In this paper, we use nonlinear programming (NLP) to determine the optimal percentage allocation of COVID-19 test kits among accredited testing centers in the Philippines that gives an equitable chance to all infected individuals to be tested. Heterogeneity in testing accessibility, population density of municipalities, and the capacity of testing facilities are included in the model. Our results show that the range of optimal allocation per testing center are as follows: Research Institute for Tropical Medicine (4.17-6.34%), San Lazaro Hospital (14.65-24.03%), University of the Philippines-National Institutes of Health (16.25-44.80%), Lung Center of the Philippines (15.8-26.40%), Baguio General Hospital Medical Center (0.58-0.76%), The Medical City, Pasig City (5.96-25.51%), St. Luke's Medical Center, Quezon City (1.09-6.70%), Bicol Public Health Laboratory (0.06-0.08%), Western Visayas Medical Center (0.71-4.52%), Vicente Sotto Memorial Medical Center (1.02-2.61%), and Southern Philippines Medical Center (≈ 0.01%). Our results can serve as a guide to the authorities in distributing the COVID-19 test kits. These can also be used for proposing additional testing centers and utilizing the available test kits properly and equitably, which helps in "flattening" the epidemic curve.

Modeling Long ncRNA-Mediated Regulation in the Mammalian Cell Cycle.

Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nucleotides that are not translated into proteins. They have recently gained widespread attention due to the finding that tens of thousands of lncRNAs reside in the human genome, and due to an increasing number of lncRNAs that are found to be associated with disease. Some lncRNAs, including disease-associated ones, play different roles in regulating the cell cycle. Mathematical models of the cell cycle have been useful in better understanding this biological system, such as how it could be robust to some perturbations and how the cell cycle checkpoints could act as a switch. Here, we discuss mathematical modeling techniques for studying lncRNA regulation of the mammalian cell cycle. We present examples on how modeling via network analysis and differential equations can provide novel predictions toward understanding cell cycle regulation in response to perturbations such as DNA damage.

Asymptotic stability of a modified Lotka-Volterra model with small immigrations.

Predator-prey systems have been studied intensively for over a hundred years. These studies have demonstrated that the dynamics of Lotka-Volterra (LV) systems are not stable, that is, exhibiting either cyclic oscillation or divergent extinction of one species. Stochastic versions of the deterministic cyclic oscillations also exhibit divergent extinction. Thus, we have no solution for asymptotic stability in predator-prey systems, unlike most natural predator-prey interactions that sometimes exhibit stable and persistent coexistence. Here, we demonstrate that adding a small immigration into the prey or predator population can stabilize the LV system. Although LV systems have been studied intensively, there is no study on the non-linear modifications that we have tested. We also checked the effect of the inclusion of non-linear interaction term to the stability of the LV system. Our results show that small immigrations invoke stable convergence in the LV system with three types of functional responses. This means that natural predator-prey populations can be stabilized by a small number of sporadic immigrants.

Antibiotic-driven escape of host in a parasite-induced Red Queen dynamics.

Winnerless coevolution of hosts and parasites could exhibit Red Queen dynamics, which is characterized by parasite-driven cyclic switching of expressed host phenotypes. We hypothesize that the application of antibiotics to suppress the reproduction of parasites can provide an opportunity for the hosts to escape such winnerless coevolution. Here, we formulate a minimal mathematical model of host-parasite interaction involving multiple host phenotypes that are targeted by adapting parasites. Our model predicts the levels of antibiotic effectiveness that can steer the parasite-driven cyclic switching of host phenotypes (oscillations) to a stable equilibrium of host survival. Our simulations show that uninterrupted application of antibiotic with high-level effectiveness (greater than 85%) is needed to escape the Red Queen dynamics. Interrupted and low level of antibiotic effectiveness are indeed useless to stop host-parasite coevolution. This study can be a guide in designing good practices and protocols to minimize the risk of further progression of parasitic infections.

From epigenetic landscape to phenotypic fitness landscape: Evolutionary effect of pathogens on host traits.

The epigenetic landscape illustrates how cells differentiate through the control of gene regulatory networks. Numerous studies have investigated epigenetic gene regulation but there are limited studies on how the epigenetic landscape and the presence of pathogens influence the evolution of host traits. Here, we formulate a multistable decision-switch model involving several phenotypes with the antagonistic influence of parasitism. As expected, pathogens can drive dominant (common) phenotypes to become inferior through negative frequency-dependent selection. Furthermore, novel predictions of our model show that parasitism can steer the dynamics of phenotype specification from multistable equilibrium convergence to oscillations. This oscillatory behavior could explain pathogen-mediated epimutations and excessive phenotypic plasticity. The Red Queen dynamics also occur in certain parameter space of the model, which demonstrates winnerless cyclic phenotype-switching in hosts and in pathogens. The results of our simulations elucidate the association between the epigenetic and phenotypic fitness landscapes and how parasitism facilitates non-genetic phenotypic diversity.

Host-parasite Red Queen dynamics with phase-locked rare genotypes.

Interactions between hosts and parasites have been hypothesized to cause winnerless coevolution, called Red Queen dynamics. The canonical Red Queen dynamics assume that all interacting genotypes of hosts and parasites undergo cyclic changes in abundance through negative frequency-dependent selection, which means that any genotype could become frequent at some stage. However, this prediction cannot explain why many rare genotypes stay rare in natural host-parasite systems. To investigate this, we build a mathematical model involving multihost and multiparasite genotypes. In a deterministic and controlled environment, Red Queen dynamics occur between two genotypes undergoing cyclic dominance changes, whereas the rest of the genotypes remain subordinate for long periods of time in phase-locked synchronized dynamics with low amplitude. However, introduction of stochastic noise in the model might allow the subordinate cyclic host and parasite types to replace dominant cyclic types as new players in the Red Queen dynamics. The factors that influence such evolutionary switching are interhost competition, specificity of parasitism, and degree of stochastic noise. Our model can explain, for the first time, the persistence of rare, hardly cycling genotypes in populations (for example, marine microbial communities) undergoing host-parasite coevolution.

A Mathematical Model of Intra-Colony Spread of American Foulbrood in European Honeybees (Apis mellifera L.).

American foulbrood (AFB) is one of the severe infectious diseases of European honeybees (Apis mellifera L.) and other Apis species. This disease is caused by a gram-positive, spore-forming bacterium Paenibacillus larvae. In this paper, a compartmental (SI framework) model is constructed to represent the spread of AFB within a colony. The model is analyzed to determine the long-term fate of the colony once exposed to AFB spores. It was found out that without effective and efficient treatment, AFB infection eventually leads to colony collapse. Furthermore, infection thresholds were predicted based on the stability of the equilibrium states. The number of infected cell combs is one of the factors that drive disease spread. Our results can be used to forecast the transmission timeline of AFB infection and to evaluate the control strategies for minimizing a possible epidemic.

Microhabitat locality allows multi-species coexistence in terrestrial plant communities.

Most terrestrial plant communities exhibit relatively high species diversity and many competitive species are ubiquitous. Many theoretical studies have been carried out to investigate the coexistence of a few competitive species and in most cases they suggest competitive exclusion. Theoretical studies have revealed that coexistence of even three or four species can be extremely difficult. It has been suggested that the coexistence of many species has been achieved by the fine differences in suitable microhabitats for each species, attributing to niche-separation. So far there is no explicit demonstration of such a coexistence in mathematical and simulation studies. Here we built a simple lattice Lotka-Volterra model of competition by incorporating the minute differences of suitable microhabitats for many species. By applying the site variations in species-specific settlement rates of a seedling, we achieved the coexistence of more than 10 species. This result indicates that competition between many species is avoided by the spatial variations in species-specific microhabitats. Our results demonstrate that coexistence of many species becomes possible by the minute differences in microhabitats. This mechanism should be applicable to many vegetation types, such as temperate forests and grasslands.

Red Queen dynamics in multi-host and multi-parasite interaction system.

In host-parasite systems, dominant host types are expected to be eventually replaced by other hosts due to the elevated potency of their specific parasites. This leads to changes in the abundance of both hosts and parasites exhibiting cycles of alternating dominance called Red Queen dynamics. Host-parasite models with less than three hosts and parasites have been demonstrated to exhibit Red Queen cycles, but natural host-parasite interactions typically involve many host and parasite types resulting in an intractable system with many parameters. Here we present numerical simulations of Red Queen dynamics with more than ten hosts and specialist parasites under the condition of no super-host nor super-parasite. The parameter region where the Red Queen cycles arise contracts as the number of interacting host and parasite types increases. The interplay between inter-host competition and parasite infectivity influences the condition for the Red Queen dynamics. Relatively large host carrying capacity and intermediate rates of parasite mortality result in never-ending cycles of dominant types.