Information

6.2.4: Review - Biology


Summary

After completing this chapter you should be able to...

  • Distinguish between point source and nonpoint source water pollution.
  • Name and describe common water pollutants, including chemical, biological, and physical pollutants.
  • Explain the mechanism of eutrophication.
  • Summarize the current state of wastewater treatment globally.
  • Describe the process of wastewater treatment, including pretreatment, primary treatment, secondary treatment, tertiary treatment, and disinfection and discharge.
  • Summarize strategies for reducing water pollution.
  • Identify sources of air pollution.
  • List common air pollutants.
  • Explain how CFCs caused ozone depletion, and global efforts to address this issue.
  • Describe the causes and consequences of acid deposition.

Water pollution may arise from a single origin (point source pollution), or it may arise from multiple dispersed sources throughout the watershed (nonpoint source pollution). Water pollutants may be chemical, biological, or physical. Oxygen-demanding waste increases biological oxygen demand and causes hypoxia, depriving aquatic organisms of oxygen. This results from eutrophication, in which excess nutrients cause algal blooms.

Pathogens are the most deadly form of water pollution. They cause waterborne diseases, killing 485,000 people every year. Resolution of the global water pollution crisis requires multiple approaches to improve the quality of fresh water. The best strategy for addressing this problem is proper wastewater treatment. Strategies to reduce water pollution in general include the Clean Water Act, remediation, and watershed management.

Air pollution can be thought of as gaseous and particulate contaminants that are present in the Earth’s atmosphere. Chemicals discharged into the air that have a direct impact on the environment are called primary pollutants. These primary pollutants sometimes react with other chemicals in the air to produce secondary pollutants. The commonly found air pollutants, known as criteria air pollutants, are particle pollution, ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. These pollutants can harm health and the environment and cause property damage.

The ozone depletion process begins when chlorofluorocarbons (CFCs) and other ozone-depleting substances (ODS) are emitted into the atmosphere. Reductions in stratospheric ozone levels lead to higher levels of harmful ultraviolet radiation, particularly UVB, reaching the Earth’s surface. The sun’s output of UVB does not change; rather, less ozone means less protection, and hence more UVB reaches the Earth. The Montreal Protocol is an international effort to phase out CFCs, and has been successful in limiting ozone depletion.

Acid deposition occurs when certain air pollutants react with atmosphere to produce nitric and sulfuric acids. It can reach Earth as various forms of precipitation or as dry particles that later react to form acid. The precursors of acid deposition result from both natural sources, such as volcanoes and decaying vegetation, and anthropogenic (human) sources, primarily emissions of sulfur dioxide (SO2) and nitrogen oxides (NOx) resulting from fossil fuel combustion. Acid deposition causes acidification of lakes and streams, contributes to the damage of trees and many sensitive forest soils. In addition, acid deposition accelerates the decay of building materials and paints, contributes to the corrosion of metals and damages human health. However, the severity of acid deposition has declined due ot regulations and technologies that limit air pollution.


It is generally appreciated that organismal phenotype is a function of both the genotype and the environment. However, most recent studies have focused on understanding the relationship between genotype and phenotype. Indeed, genetic variations are easier to quantify, data are abundant, and new methods continue to emerge. Utilizing genomic-scale gene expression and various types of molecular interaction data, several groups have started to address the challenge of identifying the molecular pathways that underlie the translation of different genotypic perturbations into corresponding phenotypic output, for example, a particular disease. In contrast, little has been done to dissect the relationship between the environment and the phenotype at the systems-biology level.

Understating the relationship between an environmental factor and a phenotype involves uncovering biomolecular pathways participating in a given environment-phenotype response. Just as various genotypic variations might lead to the same disease, various environmental perturbations often lead to the same phenotypic response. In such a case it is to be expected that the responses to these signals involve common pathways, which in turn begs several questions. What are they? What are the intermediate steps before the signals converge to such a common pathway? Which pathway is signal specific? Which molecules are involved and what is the crosstalk between different response pathways? Finally, and most important, where do we start tackling this complex problem?

Several groups have begun applying systems-level approaches to study the mechanisms that underlie cellular responses to changing environmental conditions, and these studies suggest that we are on the right path. For example, DeRisi et al. [1] investigated the gene-expression response accompanying the metabolic shift from fermentation to respiration in the yeast Saccharomyces cerevisiae. In a contrasting model-based approach, Herrgard et al. [2] used a reconstructed nutrient-controlled transcriptional regulatory network, and coupled it with a genome-scale metabolic network to predict growth phenotypes of transcription factor knockout strains. Moxley et al. [3] developed a model-based approach to correlate mRNA and metabolic flux data. Yet another approach was taken by Bradley et al. [4], who measured and analyzed the metabolomic and transcriptional responses of S. cerevisiae to carbon and nitrogen starvation. To uncover functional relations between genes and metabolites, they developed an approach based on Bayesian integration of the joint metabolomic and transcriptomic data. These and related studies helped to illuminate several aspects of molecular and/or network-level responses to a changing environment. However, as in the case of genotype-phenotype relationships, we would also like to measure and explain the dependencies between environment and higher-level phenotypes, such as the relationship between nutrients and growth.

The relationship between a cell's nutritional resources (environment) and its growth rate (phenotype), is complicated by the fact that cells affect their own environment by consuming nutrients. This problem can be circumvented by utilizing a chemostat - a device that simultaneously controls the amount of nutrients, cell population size and waste products to clamp the environment [5]. This is achieved by continuously supplying nutrients and, at the same rate, removing the culture. The level and rate of supply of a selected nutrient, the so-called limiting nutrient, is used to control the cell growth rate. For a given flux (growth rate), the steady state is achieved by (self) balancing the population size and nutrient concentration within the device. This provides a setting for studying the impact of the equilibrium nutrient concentration (corresponding to a given growth rate) on transcriptome, proteome and any other component that can be systematically measured. In this issue of BMC Biology, Steven Oliver and his colleagues (Gutteridge et al. [6]) extend the analysis of data from an earlier study by the same group using the chemostat setup [7] to focus on the effects of growth where different nutrients are limiting. A similar approach has been used by Boer et al. [8]. The data are analyzed along two distinct axes - a multivariate analysis of growth conditions (Nutrient availability × Growth rate), and an integration of data across three 'omes'.


Abstract

Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5-8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1-2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro-ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5-8.5. The results highlight that region-specific breeding efforts are required to allow for a successful adaptation to climate change.


Regression model

Traditionally, allometric analyses are conducted with ordinary least-squares (OLS) regression (e.g. Huxley, 1932 Gould, 1966 Peters, 1983 Calder, 1984 Schmidt-Nielsen, 1984). However, comparative data are likely not to meet two assumptions of this model. Firstly, because of shared phylogenetic descent, species data are likely not to represent statistically independent points. This results in overestimation of degrees of freedom and an increased Type I error rate. A large body of literature deals with both the documentation of this problem and a discussion of how to properly account for it (e.g. Felsenstein, 1985 Harvey and Pagel, 1991 Garland et al., 1992 Rohlf, 2001). Although none of the regressions presented here account for shared descent, BMR scaling patterns observed for mammals and birds are not greatly altered by the inclusion of such information (White and Seymour, 2003 McKechnie and Wolf, 2004). Secondly, OLS regression assumes that Mb is independent of the variable of interest and measured without error, which may not be the case. In such a situation, reduced major axis (RMA) regression may be more appropriate for inferring functional relationships (Sokal and Rohlf,1995). Although the classic allometry studies use OLS regression(Huxley, 1932 Gould, 1966 Peters, 1983 Calder, 1984 Schmidt-Nielsen, 1984), the use of RMA regression is becoming more common (e.g. Nunn and Barton, 2000 Green, 2001 Niklas, 2004). The RMA exponent bRMA can be calculated by dividing the OLS exponent by the square root of the coefficient of determination r 2 (Sokal and Rohlf,1995), so the difference between the regression models diminishes as r 2 increases. Where r 2 is low,however, the OLS exponent is likely to be an underestimate. Throughout this review, OLS regression results are presented(Table 1, Figs 1, 2, 3, 4, 5, 6, 7), and RMA regressions are tabulated for the main findings (Table 2).

Relationship between the percentage of large herbivores in a basal metabolic rate (BMR) data set and the coefficient of variation (CV, the standard deviation of residuals from a ln-ln allometric relationship Garland, 1984) and scaling exponent (shown ±95% CI) of the allometric relationship between BMR and body mass. Both correlations are significant (CV: r=0.92, P<0.0001 scaling exponent: r=0.85, P=0.0003). Data sources are provided in Table 1.

Relationship between the percentage of large herbivores in a basal metabolic rate (BMR) data set and the coefficient of variation (CV, the standard deviation of residuals from a ln-ln allometric relationship Garland, 1984) and scaling exponent (shown ±95% CI) of the allometric relationship between BMR and body mass. Both correlations are significant (CV: r=0.92, P<0.0001 scaling exponent: r=0.85, P=0.0003). Data sources are provided in Table 1.

Relationship between mean retention time of particles in the digestive tract (MRT, h) and body mass (Mb, g) for a range of herbivorous species that ferment in the cecum (unfilled triangles), foregut(filled circles), or colon (filled triangles). Solid line is the allometric relationship between MRT and Mb(MRT=7.3Mb 0.17±0.05 [95% CI] , r 2 =0.43, N=60). Short broken line represents the earliest appearance of particles (=MRT/3), long broken line represents the final appearance of particles (=4×MRT). Filled squares, cecum fermenting species not included in the regression analysis because standardised residuals were more than 2 s . d . from the regression mean unfilled circle, a foregut fermenting species excluded for the same reason. Insectivores, carnivores and piscivores (unfilled squares) are included for comparison, and have MRT values 2-13 times shorter than that predicted for herbivores of similar size. Data from Krockenberger and Bryden(1994), Morris et al.(1994) Stevens and Hume(1995), Caton et al.(1996), Comport and Hume(1998), Bodley et al.(1999), Campbell et al.(1999), McClelland et al.(1999), Felicetti et al.(2000), Gibson and Hume(2000), Hume et al.(2000), Pei et al.(2001).

Relationship between mean retention time of particles in the digestive tract (MRT, h) and body mass (Mb, g) for a range of herbivorous species that ferment in the cecum (unfilled triangles), foregut(filled circles), or colon (filled triangles). Solid line is the allometric relationship between MRT and Mb(MRT=7.3Mb 0.17±0.05 [95% CI] , r 2 =0.43, N=60). Short broken line represents the earliest appearance of particles (=MRT/3), long broken line represents the final appearance of particles (=4×MRT). Filled squares, cecum fermenting species not included in the regression analysis because standardised residuals were more than 2 s . d . from the regression mean unfilled circle, a foregut fermenting species excluded for the same reason. Insectivores, carnivores and piscivores (unfilled squares) are included for comparison, and have MRT values 2-13 times shorter than that predicted for herbivores of similar size. Data from Krockenberger and Bryden(1994), Morris et al.(1994) Stevens and Hume(1995), Caton et al.(1996), Comport and Hume(1998), Bodley et al.(1999), Campbell et al.(1999), McClelland et al.(1999), Felicetti et al.(2000), Gibson and Hume(2000), Hume et al.(2000), Pei et al.(2001).

Relationship between peak postfeeding resting metabolic rate(RMRpp, ml O2 h -1 ) and body mass(Mb, g). RMRpp is the highest metabolic rate observed in resting animals following feeding and is related to Mb according to RMRpp=7.91Mb 0.75±0.03 (95%CI) , r 2 =0.99, N=19. Data from Lusk(1915), Brody(1945), Gallivan and Ronald(1981), Costa and Kooyman(1984), Diamond et al.(1985), McDonald et al.(1988), MacArthur and Campbell(1994), Markussen et al.(1994), Rosen and Trites(1997), Sherwood(1997), Clements et al.(1998), Campbell et al.(1999).

Relationship between peak postfeeding resting metabolic rate(RMRpp, ml O2 h -1 ) and body mass(Mb, g). RMRpp is the highest metabolic rate observed in resting animals following feeding and is related to Mb according to RMRpp=7.91Mb 0.75±0.03 (95%CI) , r 2 =0.99, N=19. Data from Lusk(1915), Brody(1945), Gallivan and Ronald(1981), Costa and Kooyman(1984), Diamond et al.(1985), McDonald et al.(1988), MacArthur and Campbell(1994), Markussen et al.(1994), Rosen and Trites(1997), Sherwood(1997), Clements et al.(1998), Campbell et al.(1999).

Relationship between basal metabolic rate (BMR, ml O2h -1 ) and body mass (Mb, g). BMR=3.98Mb 0.686±0.014 (95% CI) , r 2 =0.94, N=571. Data were selected according to McNab (1997) and taken from White and Seymour (2003). Lineages for which basal conditions were unlikely to be achieved (large herbivores, Macropodidae, Lagomorpha, and Soricidae) were excluded for reasons discussed in the text.

Relationship between basal metabolic rate (BMR, ml O2h -1 ) and body mass (Mb, g). BMR=3.98Mb 0.686±0.014 (95% CI) , r 2 =0.94, N=571. Data were selected according to McNab (1997) and taken from White and Seymour (2003). Lineages for which basal conditions were unlikely to be achieved (large herbivores, Macropodidae, Lagomorpha, and Soricidae) were excluded for reasons discussed in the text.

Relationship between body mass (Mb, g) and standard metabolic rate (SMR, ml O2 h -1 ) for (A) euthermic and(B) hypothermic mammals, normalised to a body temperature of 36.2°C (see text for details): a Q10 of 2.8 was used for euthermic mammals, 2.4 for mammals in daily torpor (filled circles) and 2.2 for hibernating ones(unfilled circles). Equations of the regression lines: euthermic mammal SMR=4.14Mb 0.675±0.013 (95% CI) , r 2 =0.96, N=469 torpid mammal (solid line)SMR=4.81Mb 0.67±0.1 , r 2 =0.86, N=30 hibernating mammal (broken line)SMR=0.669Mb 0.87±0.08 , r 2 =0.90, N=59. Data for euthermic mammals from White and Seymour (2003),data for hypothermic ones from Geiser(1988).

Relationship between body mass (Mb, g) and standard metabolic rate (SMR, ml O2 h -1 ) for (A) euthermic and(B) hypothermic mammals, normalised to a body temperature of 36.2°C (see text for details): a Q10 of 2.8 was used for euthermic mammals, 2.4 for mammals in daily torpor (filled circles) and 2.2 for hibernating ones(unfilled circles). Equations of the regression lines: euthermic mammal SMR=4.14Mb 0.675±0.013 (95% CI) , r 2 =0.96, N=469 torpid mammal (solid line)SMR=4.81Mb 0.67±0.1 , r 2 =0.86, N=30 hibernating mammal (broken line)SMR=0.669Mb 0.87±0.08 , r 2 =0.90, N=59. Data for euthermic mammals from White and Seymour (2003),data for hypothermic ones from Geiser(1988).

Relationship between body mass (Mb, g) and maximum metabolic rate (MMR, ml O2 h -1 ) induced either by exercise (A, MMRe) or exposure to cold in a He-O2atmosphere (B, MMRc). Equations of the regression lines:MMRe=16.7Mb 0.87±0.05 (95%CI) , r 2 =0.98, N=36MMRc=31.6=Mb 0.65±0.05 , r 2 =0.92, N=70. MMRe data from Seeherman et al. (1981),Taylor et al. (1981), Koteja(1987). MMRc data from Hinds and Rice-Warner(1992), Hinds et al.(1993), Chappell and Dawson(1994), Holloway and Geiser(2001), Nespolo et al.(2001).

Relationship between body mass (Mb, g) and maximum metabolic rate (MMR, ml O2 h -1 ) induced either by exercise (A, MMRe) or exposure to cold in a He-O2atmosphere (B, MMRc). Equations of the regression lines:MMRe=16.7Mb 0.87±0.05 (95%CI) , r 2 =0.98, N=36MMRc=31.6=Mb 0.65±0.05 , r 2 =0.92, N=70. MMRe data from Seeherman et al. (1981),Taylor et al. (1981), Koteja(1987). MMRc data from Hinds and Rice-Warner(1992), Hinds et al.(1993), Chappell and Dawson(1994), Holloway and Geiser(2001), Nespolo et al.(2001).

Relationship between the scaling exponent (± 95% CI) of various mammalian metabolic rates (filled symbols: S, standard B, basal T,thermoneutral resting P, peak post-feeding F, field E, exercise induced maximum) and the elevation of the allometric relationship at a mass of 21 g(modal mammalian body mass from Blackburn and Gaston, 1998). The relationship is significant(r=0.97, P=0.001), with the data for cold-induced maximum metabolic rate (C, unfilled symbol) excluded.

Relationship between the scaling exponent (± 95% CI) of various mammalian metabolic rates (filled symbols: S, standard B, basal T,thermoneutral resting P, peak post-feeding F, field E, exercise induced maximum) and the elevation of the allometric relationship at a mass of 21 g(modal mammalian body mass from Blackburn and Gaston, 1998). The relationship is significant(r=0.97, P=0.001), with the data for cold-induced maximum metabolic rate (C, unfilled symbol) excluded.

Ordinary least-squares (OLS) and reduced major axis (RMA) allometric regression parameters for the scaling of mammalian metabolic rate

. OLS . . RMA . . .
. a . b . a . b . 95% CI .
SMR 4.17 0.675 3.85 0.689 0.013
BMR 3.98 0.686 3.61 0.706 0.014
RMRt3.66 0.712 3.33 0.729 0.013
FMR 9.99 0.73 4.53 0.75 0.04
RMRpp7.91 0.75 7.70 0.76 0.03
MMRc31.56 0.65 28.3 0.68 0.05
MMRe16.71 0.87 0.4 0.88 0.05
. OLS . . RMA . . .
. a . b . a . b . 95% CI .
SMR 4.17 0.675 3.85 0.689 0.013
BMR 3.98 0.686 3.61 0.706 0.014
RMRt3.66 0.712 3.33 0.729 0.013
FMR 9.99 0.73 4.53 0.75 0.04
RMRpp7.91 0.75 7.70 0.76 0.03
MMRc31.56 0.65 28.3 0.68 0.05
MMRe16.71 0.87 0.4 0.88 0.05

Mammalian metabolic rate (MR)=aMb b . All regressions are presented in standardised units (BMR in ml O2h −1 Mb in g). SMR, standard metabolic rate BMR, basal metabolic rate RMRt, thermoneutral resting metabolic rate FMR, field metabolic rate RMRpp, peak postprandial resting metabolic rate MMRc, cold-induced maximum metabolic rateMMRe, exercise-induced maximum metabolic rate see text for details.


Cellular Respiration: Virtual Lab

The Biology Place - Lab Bench Activity - Cellular Respiration
www.phschool.com ------> go to "The Biology Place" -----> go to LabBench ---> go to "Lab 5: Cell Respiration"

1. In this lab activity:
a) You will observe __________________________________________________________________
b) You will investigate ________________________________________________________________

2. Write the equation for cellular respiration:

3. What are the three ways in which you can measure the rate of cellular respiration?

4. Sketch a respirometer and label its important features.

5. As the organism inside the respirometer consumes oxygen, what happens to the water? _________________________
6. What happens to the CO2 that the organism produces? ____________________________

7. Experimental Setup (View the graphic)

a) fill out the table

Vial 1 Vial 2 Vial 3 Vial 4 Vial 5 Vial 6
Contents
Temperature

b) How do you ensure that each vial has an equal volume?

c. What is the purpose of the vial with only glass beads?

a) What is the equation to determine the rate of respiration?

b) What is X _______________ What is Y _______________

9. Read the respirometers and determine the rate of respiration. Show your calculations

a) Describe the relationship between temperature and consumption of oxygen.

b) Calculate the rate of oxygen consumption for germinating corn at 12 degrees. (Show calculations)

c) Based on the graph, would you conclude that non germinating seeds respire?

11. Extension (You do not need the computer to finish this section, do as homework)

A cricket is placed in a respirometer and data taken at three temperatures. The following table shows the data collected.

Temperatures
Time (min) 10 degrees 18 degrees 25 degrees
0 0.0 0.0 0.0
5 0.25 0.6 0.9
10 0.5 0.9 1.4
15 0.7 1.2 1.8
20 0.9 1.6 2.4

a ) Graph the data

b) Determine the rate of respiration for each of the three temperatures. (Show work)

c) Write a paragraph stating your conclusions

/>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


METHODS

Study area

The Flemish Cap (Figure 1) is an oceanic bank about 600 km to the east of Newfoundland in an area beyond national jurisdiction and in the Northwest Atlantic Fisheries Organization (NAFO) regulatory area. It has high ecological productivity that supports abundant fish populations and provides a variety of ESs (Grehan et al., 2018 ). Flemish Cap was historically a productive fishing ground supporting various fisheries, such as Greenland halibut (Reinhardtius hippoglossoides), American plaice (Hippoglossoides platessoides), cod (Gadus morhua), redfish (Sebastes spp.), grenadier (Macrourus berglax), yellowtail flounder (Limanda ferruginea), capelin (Mallotus villosus), skate (Dipturus laevis), shrimp (Pandalus borealis), squid (Illex spp.), and so on (Grehan et al., 2018 ). Many commercial and noncommercial species have declined substantially as a consequence of overexploitation, habitat degradation, and climate change (Howell & Casas, 2017 Pérez-Rodríguez et al., 2012 ).

Currently, the main human activities in the region are fisheries, shipping, undersea cable routes, scientific research, and hydrocarbon exploration (Grehan et al., 2018 ) (Figure 1). Yet, there is no integrated spatial management plan for the Flemish Cap and only 1 active fishing sector management plan, the NAFO management plan. There are potential opportunities in the Flemish Cap for growth in marine activities in existing and potential economic sectors, such as increased oil and gas exploitation, bioprospecting, and new fisheries (Grehan et al., 2018 ). The development of such economic activities could generate more jobs internationally but could also lead to negative effects on ecosystems in the area.

Discrete choice experiment

We used DCE to elicit the public's preferences for protecting the environment in the Flemish Cap. Respondents were presented with a sequence of hypothetical choice tasks, each containing a set of competing policy alternatives described by several attributes taking on a finite number of levels. When respondents selected their preferred alternative that was assumed to maximize their utility, they implicitly revealed their trade-offs between the levels of the attributes in all the alternatives presented in the choice task. Based on the choice responses, we estimated the utility function up to a probability and derived welfare measures, such as the public's marginal WTP a higher tax rate to obtain an improvement in the environmental quality of the high seas. Survey questions and choice tasks are in Appendix S3. Each choice task included 3 alternatives: a status quo (SQ), with current attribute levels of provision, and the 2 experimentally designed alternatives (with current and improved attribute levels of provision).

Of the 5 attributes (Table 1) that appeared in each choice task (shown in Appendix S3), 3 were associated with the environmental aspects (health of fish stocks, amount of marine litter, and size of marine protected area [MPA]), 1 was associated with economic development (marine economy jobs), and 1 was related to the cost of the proposed policy in the form of an annual income tax increase, expressed in currency units corresponding to each country where the survey was conducted.

0 (for status quo [SQ] option only), 10, 20, 40, 60, 80, 110

0 (for SQ option only), 100, 150, 300, 450, 650, 850

0 (for SQ option only), 5, 10, 20, 30, 40, 60

  • * Levels of the cost attribute used in 3 surveys (in national currency) adjusted for differences in income levels between the countries based on the purchasing power parity index of 1.245, 10.142, and 0.7 for Canada, Norway, and Scotland, respectively (1, reference level) (data from OECD [ 2018 ]).

Survey design and sampling

The surveys were implemented online by the market research company YouGov, which drew from a registered online panel of respondents in Canada, Norway, and Scotland in October and November of 2019. YouGov has a panel management system that defines quotas based on representation by gender, age, and geography. Their system sent out random invitations to respondents in their panel until the quotas were satisfied. Response rates varied, but the company expected response rates to be minimum 30%. Details of survey design and sampling are in the Appendix S1. The surveys were approved by the research ethics committee at the University of Edinburgh and by the Norwegian Centre of Research Data.

Models

To identify the sociodemographic, attitudinal, and spatial determinants of WTP for high-seas ecosystems protection, we applied a 2-stage approach that may increase the explanatory power of welfare estimates (Campbell, 2007 Scarpa et al., 2011 Yao et al., 2014 ). This modeling approach was employed because a regression on conditional mean WTP is better suited to explore systematic effects of the explanatory variables on WTP variation than exploring these effects on random parameters as in a hybrid choice modeling approach (Yao et al., 2014 Zawojska et al., 2019 ). First, a model allowing for preference heterogeneity, a mixed logit (MXL) model, was used to estimate individual WTP values (Hensher et al., 2015 ). Next, these individual WTP estimates were regressed on determinants of WTP for high-seas ecosystems protection.

Mixed logit model

(1) where is the utility of individual n obtained from choosing alternative i in choice situation t is a vector of observed variables related to the attributes is a vector of parameters associated with the attributes representing the individual's tastes is a random term with 0 mean whose distribution over individuals and alternatives depends on underlying parameters and observed data associated with alternative i and individual n and is a random term with 0 mean that is independent and identically distributed (iid) over alternatives and depends on neither underlying parameters nor data (Hensher et al., 2015 ).

To allow for heterogeneous preferences among respondents, all noncost attribute parameters were specified as random following a normal distribution. After evaluating the results from the various specifications of distributional assumptions, we found the assumption of a normal distribution of noncost random parameters to fit our data well. An assumption of a log-normally distributed cost parameter resulted in unrealistic (very high) WTPs. Hence, the cost parameter and the alternative-specific constant (ASC) in the model were assumed to be fixed across respondents. Additionally, the fixed cost parameter assisted in the computation of WTP values.

Linear regression of WTPs

To determine the factors influencing WTP for protecting high-seas ecosystems, we used the standard ordinary-least-squares (OLS) regression and a panel random effect (RE) regression. In the MXL model, the noncost attribute variables were dummy coded with the reference level being the SQ level. The estimated results (see Appendix S2) showed that the population mean WTP was largest for the highest improved level related to each noncost attribute. Therefore, we selected the individual WTP corresponding to the highest improved level for each of the 4 attributes (so-called attribute level) and used them as dependent variables in the OLS regression to explore the determinants of the attribute-level WTP. We then regressed these 4 attribute-level values (i.e., that were pooled and used as the dependent variable) on individual characteristic covariates in the form of a 4-period panel to account for the fact that these conditional mean estimates were correlated for the same respondent.

(2) where is willingness to pay for attribute-level a for respondent n is an intercept term capturing the average WTP for the concerned attribute level in the case of OLS, whereas for the RE, it captures the average WTP for the reference attribute level is a vector of indicator variables for k minus 1 attribute level (i.e., specified for the RE regression) is a vector of covariates and are vectors of parameters to be estimated ( is the error term, where is the individual-specific error term (i.e., specified for the RE regression) and is the usual error term with properties of zero mean, serially uncorrelated (across a), and homoscedasticity.

A summary of the explanatory variables used in the linear regression model is in the second part of Table 1. In addition to the sociodemographic variables, the attitudinal variables that described the respondents’ awareness of the Flemish Cap, their perception regarding the current status and management of the Flemish Cap, the perceived personal effects on the respondent when the Flemish Cap ecosystems change, and the confidence of respondents when they made their choices were included as regressors to explain the variation in individual WTP of the sample of respondents. The response scale for these variables included a don't know or don't care option (Table 1), allowing an indefinite statement of the attitude. When analyzing the data, we treated don't know or don't care responses as the outermost level followed by the definite negative category, as suggested by Zawojska et al. ( 2019 ). Respondents’ locations (e.g., nationality) were also included in order to capture the distance-decay effect on individual WTP variation. The explanatory variables in this model were all dummy coded. We used a p value ≤5% level to determine significant results.


Cancer

any malignant, cellular tumor. For specific types, see under the name, such as breast cancer or lung cancer . adj., adj can´cerous.

The term cancer encompasses a group of neoplastic diseases in which there is a transformation of normal body cells into malignant ones. This probably involves some change in the genetic material of the cells, deoxyribonucleic acid (DNA). oncogenes are the genes that organisms have evolved to regulate growth and repair of tissues. They are genetic codes for the proteins that function as signals that cells send and receive to regulate proliferation. These oncogenes are the targets of carcinogens . mutation and transformation of oncogenes may permanently affect a cell's ability to control cell growth. Damage to the cell's genetic material may be caused by carcinogenic agents. Normal cell lines can be transformed into cancer cells by viruses , chemical carcinogens , and radiation . Transformed cell lines have the ability to develop into malignant neoplasms. Transformed cells may also be recognized by other characteristics which include altered antigenicity, diminished contact inhibition, reduced requirements for certain nutrients, and the ability to grow in suspension. The altered cells pass on inappropriate genetic information to their offspring and begin to proliferate in an abnormal and destructive way. Normally, cells reproduce regularly to replace worn-out tissues, repair injuries, and allow for growth during the developing years. After these processes have taken place, cellular reproduction stops. Clearly the body in its normal processes regulates cell growth in an orderly manner. In cancer, there is no regulation and cell reproduction and growth is disorderly. The dangers of cancer are related to this chaotic reproduction of malignant cells.

As the cancer cells continue to proliferate, the mass of abnormal tissue that they form enlarges, ulcerates, and begins to shed cells that spread the disease locally or to distant sites. This migration is called metastasis . Some cells penetrate neighboring tissues, destroying normal cells and taking their place. Others can enter the blood stream and lymphatic vessels and be carried along in the fluid to another part of the body. Another way malignancy can be spread is by entering a body cavity and coming in contact with a healthy organ however, this is not common.

Causes . It is doubtful that one process is involved in the etiology of all cancers. The exact cause of conversion of normal cells into cancerous ones is still not completely understood. An important factor is permanent alteration in the DNA of the cell, which is passed on to subsequent generations, but we do not know what triggers the change in DNA structure and why some people succumb to a cancer and others do not. Cellular immunity undoubtedly plays some part in one's ability to stop the growth of cancer cells it is believed by some that most persons develop many small cancers in their lifetime but do not develop clinical signs because their defense mechanisms destroy the malignant cells and prevent their replication.

Oncologists recognize that environmental, hereditary, and biological factors all play important roles in the development of cancer (see table). Environmental causes are believed to account for at least 50 per cent and perhaps, in some types, as much as 80 per cent of all cancers. For example, cigarette smoking is directly related to approximately 90 per cent of all cases of lung cancer . Other environmental carcinogens include industrial pollutants and radiation. Among the chemical carcinogens are arsenic from mining and smelting industries asbestos from insulation, at construction sites and power plants benzene from oil refineries, solvents, and insecticides and products from coal combustion in steel and petrochemical industries. Each year new products that in all probability are carcinogenic are being produced by industrial operations. A major concern is the occupational and environmental hazards these chemicals present to those who work in or live near these plants.

Radiation from prolonged exposure to the ultraviolet rays of the sun or from injudicious use of diagnostic and therapeutic procedures involving x-rays and radioactive substances is also a significant factor in the incidence of cancer, particularly that of the skin, bone marrow, and thyroid.

Hormones , especially the synthetic estrogens given to prevent spontaneous abortion , are directly related to some cancers of the female reproductive organs.

Viruses as causal agents in the development of cancer have been subjected to intensive research efforts in recent years. The epidemiologic evidence is strongest for a relationship between hepatitis B virus and hepatocellular carcinoma and between human T-lymphotropic virus (HTLV)-1 and T-cell lymphoma . Both have a geographic distribution of cancer prevalence and viral infection as well as case-by-case associations. The association between burkitt's lymphoma and epstein-barr virus (EBV) is likewise strong, except that there seems to be a need for an associated immunodeficiency state, such as that induced by chronic malaria . Similarly, the association between EBV and high-grade lymphoma in Western countries seems to require that an immunodeficiency state be present, either congenital or induced by the human immunodeficiency virus (HIV) or a drug such as cyclosporine .

The intriguing fact has been noted that viruses are capable of introducing new genetic material into a normal cell and transforming it into a malignant one, and that cell reproduction may be altered when viruses interact with such carcinogens as chemicals and radiation. Recent studies have shown that an extracellular enzyme, reverse transcriptase , plays an important role in the transmission of genetic information to the cell and thereby facilitates the reproduction of cancer cells.

The incidence of cancer in certain populations suggests that other factors are important in its development. It is known, for example, that some families show a high incidence of malignancy among their members, but there is no definite hereditary pattern. There also is a high incidence of cancer in persons receiving drugs for immunosuppression, yet cancer itself is immunosuppressive. It is suggested that prolonged suppression of the body's immune response may eventually impair its ability to distinguish between self and nonself and thus render it unable to destroy malignant cells. When cancer itself acts to suppress the immune response, it may be the result of an overwhelming demand on the body to destroy more foreign cells than it is prepared to cope with at any given time.

Aging is another factor to consider in development of malignancy. Although cancer can occur at any age, older persons are more susceptible, perhaps because their powers of adaptability are weakened and they have been exposed to carcinogens longer than have younger persons.

Classification . Cancers are classified on the basis of two factors: the type of tissue and the type of cell in which they arise. Using this classification system, it is possible to identify over 150 types of cancer in humans. In the classification of cancers according to the type of tissue from which they evolve, there are two main groups, sarcomas and carcinomas . Sarcomas are of mesenchymal origin and affect such tissues as the bones and muscles they tend to grow rapidly and to be very destructive. The carcinomas are of epithelial origin and make up the great majority of the glandular cancers and cancers of the breast, stomach, uterus, skin, and tongue. Cell type affects the appearance, rate of growth, and degree of malignancy. Thus, classification of tumors according to the type of cell from which they are derived is important in deciding the course of treatment for a specific malignancy.

Staging. An approach to describing and categorizing malignant tumors has been developed by the International Union Against Cancer (UICC) and the American Joint Committee on Cancer (AJCC). It is hoped that by standardizing the classification and staging of tumors, treatment protocols can be established and end results reporting can be utilized to determine the effectiveness of the suggested treatment. Whereas classification of tumors refers to the anatomical and histological descriptions of the tumor (see above), staging refers to the extent of the tumor. The three components of the staging system are the primary tumor (T), regional nodes (N), and metastasis (M). Subscripts may be used to describe the extent to which the malignancy has increased in size, its involvement of regional nodes, and its metastatic development (see table). For example, a tumor may be described as T1N2M0. dukes' classification is a system of staging colorectal tumors, based on the depth of invasion and degree of metastasis.

Precancers. Some potentially dangerous cancers appear first in the form of harmless changes in the body's tissues. The danger lies in the fact that such changes have a tendency to become malignant hence they are known as precancers . Among them are sores that appear as thickened white patches ( leukoplakia ) in the mouth and on the vulva, some moles , and any chronically irritated area on the skin or the mucous membranes of the mouth and tongue. polyps are also possible precancers.

Prevention . Because environmental conditions play an important role in the etiology of many cancers, prevention is aimed at identifying carcinogens, educating the general public about them, and encouraging their avoidance. Equally important, if not more so, is recognition of causative factors related to life style and personal habits. Perhaps the best example of this is the relationship between smoking and lung cancer . When heavy consumption of alcohol is combined with cigarette smoking, the risk for cancer of the larynx, esophagus, and mouth is greatly increased.

Nutritional balance is also important in the prevention of cancer. Certain foods and food additives contain specific carcinogenic agents. Nutritional deficiency can lower resistance and increase the risk of certain types of cancers. The decrease in incidence of stomach cancer in most Western countries may possibly be the result of an increase in consumption of fruits and vegetables, since vitamin B12 deficiency ( pernicious anemia ) is known to be related to increased incidence of stomach cancer.

Studies have shown that a relationship exists between obesity and cancer, and between dietary excess, particularly consumption of large amounts of fats, and certain types of cancers. In general, overweight women are at increased risk for cancer of the endometrium, gallbladder, and kidney. Cancers associated with a high dietary intake of fat, with or without obesity, are those affecting the breast, ovary, endometrium, prostate, colon, and pancreas. Although neither saturated nor unsaturated fats are themselves carcinogenic, they act on the endocrine system and affect hormonal activity. The relationship of fat consumption to colon cancer is thought to be due to the effect of bile acids and their metabolites, which have been shown to act as tumor promoters in laboratory animals. In humans, patients with cancer of the colon typically have elevated levels of bile acid metabolites. Studies of various populations throughout the world have shown that bowel cancer is more prevalent among groups who eat large amounts of fat and very little food fiber. Hence the American Cancer Society recommends a low fat, high fiber diet for Americans.

The judicious use of hormones for therapeutic purposes also can reduce the incidence of some cancers. The widespread use of diethylstilbestrol (DES) to prevent threatened or habitual abortion and premature labor, beginning in the 1940s, eventually resulted in development of vaginal and cervical cancer in a significant number of the female offspring of women who took the drug while pregnant. As was previously mentioned, estrogens prescribed for relief of menopausal symptoms have been implicated in cancer in women. It is recommended that the lowest possible therapeutic dose be given to relieve the symptoms of menopause and prevent osteoporosis.

Cancer of the skin and malignant melanoma are related to prolonged exposure to the ultraviolet radiation in sunlight. The incidence of cancer of the skin is increasing in those persons who value a deep suntan and spend a significant amount of time engaged in outdoor leisure activities. Also at risk are those whose work requires that they be exposed to sunlight for prolonged periods of time, such as farmers.

Since most occupational cancers are preventable, increased awareness on the part of industry and the provision of a safe workplace environment can decrease the incidence of many kinds of cancer. It is also necessary for workers to cooperate with management in reducing exposure to carcinogens by complying with rules for preventive measures.

Ultimately, the prevention of cancer depends upon knowledge of each person's risk factors for development of cancer, and that person's decision to avoid whenever possible those habits and practices that predispose to the disease. There also should be frequent examination and monitoring of those who are known to be at greater risk.

Detection . In addition to routine cancer-related checkups by a health care provider for early detection of cancer, self-examination and awareness of the early danger signs of cancer are suggested as means by which lay persons can participate in detecting it in its earliest stages.

Monthly self-examination of the breast is advocated for all adult women, including those who are postmenopausal. Monthly self-examination of the testes is recommended for all males, particularly those in the age group most at risk for testicular cancer, that is, between the ages of 15 and 34 years.

Another self-administered screening technique is the test for occult blood, a symptom of colorectal cancer. This requires only that a smear of fecal material be applied to a slide, which is sent to a clinical laboratory for examination. To avoid a false positive reading, the person participating in the test is given instructions regarding ingestion of meat and other foods that could interfere with accurate test findings.


How to Add Parentheses to Make a Statement True

Parentheses are used in math equations to prioritize the order in which a problem must be solved. Use the basic principles of math to determine where parentheses should go when completing an equation and learn to apply the basic fundamentals of math to break down a multi-step equation, turning a complicated question into a simple one.

Write out the equation on a piece of paper in large, easy-to-read numbers to prevent unnecessary errors from sloppy handwriting. Our equation will be 1+2x3-4=-3. Make sure all symbols are easy to read, and recheck your equation before beginning to ensure all information has been written correctly.

Put parentheses around the first two numbers provided to create an equation in this case (1+2) x 3-4. Use PEMDAS to determine the order of operations. PEMDAS, or Please Excuse My Dear Aunt Sally, is an acronym signifying the correct order that all math equations should be solved with. P is for parentheses, E is for exponents, M is for multiplication, D is division, A represents addition and S is for subtraction.

Work out the problem in the parentheses, (1+2). Take the answer, 3, and complete the equation, moving from left to right. So, multiply 3 by 3 to get 9. Subtract 4 from 9 to get 5. Parentheses are incorrect around the first two numbers of the equation because your answer is not -3.

Rework the problem by putting parentheses around the next two numbers in the equation 1+ (2x3) - 4. Work it out using the PEMDAS order of operations. You answer will be 3 and still incorrect. Move the parenthesis to go around the last two numbers of the equation now your answer will be -3.

Check your answer. Write out your equation, and do it again to ensure all math was done correctly and in the right order.


Preface

In the last decade of the 20th century, computer science and biology both emerged as fields capable of remarkable and rapid change. Moreover, they evolved as fields of inquiry in ways that draw attention to their areas of intersection. The continuing advancements in technology and the pace of scientific research present the means for computing to help answer fundamental questions in the biological sciences and for biology to demonstrate that new approaches to computing are possible.

Advances in the power and ease of use of computing and communications systems have fueled computational biology (e.g., genomics) and bioinformatics (e.g., database development and analysis). Modeling and simulation of biological entities such as cells have joined biologists and computer scientists (and mathematicians, physicists, and statisticians too) to work together on activities from pharmaceutical design to environmental analysis.

On the other side, computer scientists have pondered the significance of biology for their field. For example, computer scientists have explored the use of DNA as a substrate for new computing hardware and the use of biological approaches in solving hard computing problems. Exploration of biological computation suggests a potential for insight into the nature of and alternative processes for computation, and it also gives rise to questions about hybrid systems that achieve some kind of synergy of biological and computational systems. And there is also the fact that biological systems exhibit characteristics such as adaptability, self-healing, evolution, and learning that would be desirable in the information technologies that humans use.

Making the most of the research opportunities at the interface of computing and biology&mdashwhat we are calling the BioComp interface&mdashrequires illuminating what they are and effectively engaging people from both computing and biology. As in other contexts, the challenges of interdisciplinary education and of collaboration are significant, and each will require attention, together with substantive work from both policy makers and researchers. At the start of the 1990s, attempts were made to stimulate mutual interest and collaboration among young researchers in computing and biology. Those early efforts yielded nontrivial successes, but in retrospect represented a Version 1.0 prototype for the potential in bringing the two fields together. Circumstances today seem much more favorable for progress. New research teams and training programs have been formed as individual investigators from the respective communities, government agencies, and private foundations have become increasingly engaged. Similarly, some larger groups of investigators from different backgrounds have been able to

obtain funding to work together to address cross-disciplinary research problems. It is against this background that the committee sees a Version 2.0 of the BioComp interface emerging that will yield unprecedented progress and advance.

The range of possible activities at the BioComp interface is broad, and accordingly so is the range of interested agencies, which include the Defense Advanced Research Projects Agency (DARPA), the National Science Foundation (NSF), the Department of Energy (DOE), and the National Institutes of Health (NIH). These agencies have, to varying degrees, recognized that truly cross-disciplinary work would build on both computing and biology, and they have sought to advance activities at the interface.

This report by the Committee on Frontiers at the Interface of Computing and Biology seeks to establish the intellectual legitimacy of a fundamentally cross-disciplinary collaboration between biologists and computer scientists. That is, while some universities are increasingly favorable to research at the intersection, life science researchers at other universities are strongly impeded in their efforts to collaborate. This report addresses these impediments and describes some strategies for overcoming them.

In addition, this report provides a wealth of well-documented examples. As a rule, these examples have generally been selected to illustrate the breadth of the topic in question, rather than to identify the most important areas of activity. That is, the appropriate spirit in which to view these examples is &ldquolet a thousand flowers bloom,&rdquo rather than one of &ldquofinding the prettiest flowers.&rdquo It is hoped that these examples will encourage students in the life sciences to start or to continue study in computer science that will enable them to be more effective users of computing in their future biological studies. In the opposite direction, the report seeks to describe a rich and diverse domain&mdashbiology&mdashwithin which computer scientists can find worthy problems that challenge current knowledge in computing. It is hoped that this awareness will motivate interested computer scientists to learn about biological phenomena, data, experimentation, and the like&mdashso that they can engage biologists more effectively.

To gather information on such a broad area, the committee took input from a wide variety of sources. The committee convened two workshops in March 2001 and May 2001, and committee members or staff attended relevant workshops sponsored by other groups. The committee mined the published literature extensively. It solicited input from other scientists known to be active in BioComp research. An early draft of the report was examined by a number of reviewers far larger than usual for National Research Council (NRC) reports, and the draft was modified in accordance with their extensive input, which helped the committee to sharpen its message and strengthen its presentation.

The result of these efforts is the first comprehensive NRC study that suggests a high-level intellectual structure for federal agencies for supporting work at the BioComp interface. Although workshop reports have been supported by individual agencies on the subject of computing applied to various aspects of biological inquiry, the NRC has not until now undertaken a study whose intent was to be inclusive.


Watch the video: Notes for IB Biology Chapter and D4 (January 2022).