Experiment to determine the number of infective phage particles in a sample using viral titration.
Methods/Procedure
Figure 1. Conceptual scheme of virus suspension dilution.
The peculiarities of bacteriophage reproduction are related to their intensive expansion during cultivation, so a procedure of sequential tenfold dilution was used (Figure 1): a total of nine test tubes with molten tryptone-soft agar. A 200 mL aliquot of E.coli was added to each of the tubes, and after thorough mixing in a water bath, the samples were transferred to prelabeled plates.
Figure 2. Example of three of nine prepared plates with bacteriophage suspensions.
Results
Since the basis of the experiment was to measure the number of plaques on previously prepared plates, the only direct measurement was to count the number of such spots on the entire plate, and only the minimum final concentrations were used to ensure reliability. The second column of Table 1 below reflects the counted quantities, with the third column giving the relative plaque content per unit volume (mL).
Dilution of bacteriophage
Plague number
PFU/ml
10-6
22
2.2 x10-5
10-7
16
1.6 x10-6
10-8
11
1.1 x 10-7
10-9
14
1.4 x 10-8
10-10
3
3 x 10-10
Table 1. Results of absolute and relative plaque counts.
Discussion/Conclusion
The purpose of the present study was to use a plaque quantification protocol to determine the active bacteriophage units in the samples. Bacteriophages are typical viruses whose mechanism of infection is directed purely at bacteria, which means that bacteria are directly targeted by bacteriophages (Hill, 2019). The release of cellular material by bacteriophages occurs through a complex procedure of lysis of the bacterial cell wall, injection of genetic material, and subsequent stages of transcription and translation of recombinant proteins (Principi et al., 2019). All this is not aimed at destroying the bacterial cell but at creating another farm for autonomous viral reproduction.
There are a vast number of bacteria in the human body, many of which are symbionts. One of these is E. coli, a symbiotic bacterium of the intestinal system that helps process complex carbohydrates (E. coli, 2018). Obviously, E. coli can be susceptible to the deleterious effects of bacteriophages. There is a separate cluster of such viruses, Coliphages, whose action targets E. coli directly (Pacífico et al., 2019). As a consequence, disruption of the viability of symbiotic bacteria, as an example, affects the health of the individual and brings deterioration of the quality of life.
A plaque counting procedure on bacteriophage growth plates is used for titrimetric microbiological analysis tasks. Plaques are to be understood as spot-like formations in which active growth of the pathogen is observed. For those cases where the concentrations of the agar medium are known, it becomes possible to determine the predicted content of the virus particles in the initial solution, which was demonstrated in this experiment. In fact, it should be clarified that the use of the PFU (or PFU/ml) measure is an analogous measure for colony forming units (CFU) for bacteria, which means that a parallel can be drawn between the two concepts (How to calculate CFU, 2019). PFU is thus used to quantify the viable bacteriophages that can reproduce in this agar. It is worth emphasizing, however, that PFU does not measure the absolute number of viral particles in the agar but rather only those that are viable and can form plaques.
How to calculate CFU (colony forming unit) (2019) Web.
Pacífico, C., Hilbert, M., Sofka, D., Dinhopl, N., Pap, I.J., Aspöck, C., Carriço, J.A. and Hilbert, F. (2019) ‘Natural occurrence of Escherichia coli-infecting bacteriophages in clinical samples,’ Frontiers in Microbiology, pp. 2484-2490.
Principi, N., Silvestri, E. and Esposito, S. (2019) ‘Advantages and limitations of bacteriophages for the treatment of bacterial infections,’ Frontiers in Pharmacology, 10, pp. 513-518.
The Nobel Prize in Economic Sciences 2021 primarily concerns the labor market, and how different variables affect it. The winners – David Card, Joshua Angrist, and Guido Imbens – have demonstrated how natural experiments might be utilized in empirical research (“The prize in economic sciences”, 2021). The authors have acknowledged the efficiency of natural experiments and have developed a framework to differentiate cause and effect in similar studies. This contribution might significantly change the scientific approach to phenomena in various spheres, including economics, psychology, sociology, and many others.
The two things in the research that impressed me the most is the complexity of the natural experiments and the methods to simplify the research. For instance, a natural experiment is significantly complicated by the fact that the researcher only knows the numerical data but is not aware of subjects’ motives. However, Angrist and Guido have proposed to use the “instrumental variables” method, which allows to roughly estimate the impact of the motives and, thus, simplify the research.
Concerning the natural experiments in the Kingdom, I believe that the estimated findings would be different due to the economic, cultural, and regional differences. Namely, the results might be less illustrative because of the territorial integrity of the country, compared to the United States; thus, there are fewer variables that allow determining the control and treatment groups on the national scale. Nevertheless, it does not imply the flaws of the framework or natural experiments. In other words, I believe that the findings would be different; however, they would still be highly useful to the national economy in case it is possible to find a suitable natural experiment.
Lastly, I believe that some ongoing projects within 2030 could be considered as a natural experiment in case of abrupt changes. The article transparently indicates that natural experiments are not rare; in contrast, they occur frequently due to various policies and systems, which clearly define the control and treatment groups (“The prize in economic sciences”, 2021). From these considerations, a 10-year period of the 2020s is a sufficient period of time to conduct natural experiments at least to some extent.
If the action of all forces applied to the body is compensated, then the body is said to be in a state of equilibrium. However, a solid body may continue to rotate about its axis even if the sum of all forces is zero. In this situation, a torsional momentum occurs, which has a vector calculus, as shown in equation (1). Thus, the direction of the torque with respect to the point is essential and shows the ability of the body to rotate in the direction of the resultant vector. The scalar value of this torque is determined through the sine of the angle, as shown in equation (2).
Figure 1. Schematic representation of torque: the direction of rotation occurs according to the resultant force F (Jones, 2018).
Objectives
Determine the torque for a solid.
Graphically visualize the dependence of force on angle.
Materials
Force Scale.
Hanger.
Protractor.
Ruler.
Weights.
Preliminary setup and questions
This experiment uses different masses as tools to test the torque theory. For each of the symbols used, the following is a terminology description:
F – physical force.
r→ – radial vector.
r – the modulus of the vector, or its length.
θ – angle in degrees between the radial vector and the resultant force.
Procedure
The entire experiment was conventionally divided into two parts. In the first one, equilibrium was studied for the setup used. In the second part, dependences for torque as a function of radial vector and force were determined.
The procedure of the first part. The first weight of a given mass is suspended from the rig, with the holder tilted toward the placement of the weight. The equilibrium condition is reached by selecting the second weight of the necessary mass. The holder, in this case, is characterized by the torque, which is calculated through the levers of force.
The procedure of the second part. For the second part of the experiment, the torque produced by one weight in the vertical position is evaluated. For this purpose, the values of forces, lever length, and torque for the first load and the distance and angle value for the second load are recorded for the retracted state (springs are used). Based on the results of all measurements, a data table is created, and calculations are made.
Data tables
TRIAL
M1
M2
R1
R2
#1
200
100
6
12
#2
200
70
6
18
#3
200
200
6
6
Table 1. Torque measurements for different weights.
TRIAL
F2 (N)
Theta
0
0.5
90°
1
1.0
45°
2
1.4
40°
3
1.7
55°
4
4.3
20°
Table 2. Measurements of the second force as a function of theta angle.
Analisys
Figure 2. Diagram of the second force vs. theta angle.
Analysis of the graphical results shown in Figure 2 shows that there is a moderate linear correlation between the theta angle and the second force, which means that we can say that, in general, when the degree value between the radial vector and the force vector increases, there is a decrease in the second force by 0.05 units. Additional results can be found for the first part of the experiment, which evaluated the relationship between distance and mass suspended on the holder for each of the three attempts. For the first attempt, equations (3) and (4) show r calculations. Similar calculations are given for the second and third attempts, respectively.
It is well visible that the torques for each of the tests are generally similar, which means that the masses and distances were chosen so as to achieve an equilibrium rotation of the body each time.
Conclusion
In the current experiment, it has been shown that using theoretical calculations of torque through the mass of the suspended weight and distance is of applied value. In other words, it is possible to use different masses and distances of the arm to determine torque. In the present work, these variables have been appropriately manipulated in order to determine the equilibrium rotation of a solid. In other words, the torque r was identical in five of the six attempts. In addition, it was shown that an almost linear relationship was found between the angle between the radial vector and the resultant force and the second force.
The purpose of the given experiment was to determine if bean beetles have bean preferences for oviposition. To test this hypothesis, a Petri dish was covered with a single layer of beans, and 5 male and 5 female beetles were introduced for both the control and experimental groups. In such a way, a dense culture was produced that could sustain several generations. The Petri dish with beans and beetles was placed in the assigned area in the lab. The temperature of 230C was favorable for mating and, consequently, for the production of eggs. The environmental variable chosen for testing was the type of bean. In total, there were 182 beans of six different types. To determine if beetles had bean preferences for oviposition, the number of eggs laid on each bean was counted. Then, the chi-squared test was performed to determine if the differences between the observed and the expected number of eggs laid on each bean were statistically significant.
There were 29 eggs laid on the red beans in the observed group, though the expected number of eggs was 30. There were 14 eggs laid on kidney beans, though the expected number of eggs was 30. There were 51 eggs laid on the lentil beans in the observed group, though the expected number of eggs was 30. There were 81 eggs laid on the pinto beans in the observed group, though the expected number of eggs was 30. There were only 1 egg laid on the baby limb and 6 eggs laid on black-eyed beans, though the expected number of eggs was 30 for each bean type.
The chi-squared test was performed, and the chi-square value was equal to 157.2. Since the variable (a bean type) has six levels, the number of degrees of freedom is five. The obtained chi-square value is greater than the critical chi-square value of 11.07. Thus, it is possible to conclude that bean beetles have bean preferences for oviposition at the significance level of 0.05. It can be seen that there is a significant preference for pinto beans and lentil beans, compared to baby limb and black-eyed beans.
At the next stage of the experiment, it was tested how a mother’s choice of substrate affected both her offspring’s survival rate and her reproductive success. To analyze this, the number and sex of beetles that hatched in each dish were recorded in the table. It appeared that even though there were 182 eggs in total, none of the beetles hatched from those eggs. Therefore, for both the experimental group and the control group, the number of males and females hatched was equal to zero. Accordingly, the total number of beetles hatched per bean type was equal to zero. Thus, the hatch rate per bean type is equal to 0%. Since there were no adults alive, there was no point in determining if the sex ratio (males to females) is 50/50 using a chi-squared statistical test.
In summary, it has been proven that the oviposition behavior of female beetles depends on the type of bean. Specifically, lentil and pinto beans are the most preferred ones, and baby limb and black-eyed beans are the least preferred ones. However, the experiment did not aim to give a theoretical explanation of such oviposition preferences. Despite the fact that the beetle’s choice of the bean could impact the survival rate of her offspring, this assumption was not tested because there were no adults alive. The experiment is interesting as it can further the knowledge about the selection process for the female beetles.
Data Visualizations
Figure 1 shows how many eggs female beetles laid on each type of bean. It can be seen that female beetles preferred pinto and lentil beans over the other bean types. However, this assumption has to be tested using a chi-squared test to determine if the differences that are visible have statistical significance. Figure 2 compares the observed number of eggs to the expected number of eggs.
Fig. 1. The number of eggs laid on each type of bean (observed number).Fig. 2. The observed number of eggs vs. the expected number of eggs.
Figure 3 shows the chi-squared distribution for the five degrees of freedom. At the significance level of 0.05, the critical chi-square value is 11.07. The calculated chi-square value is greater than the critical chi-square value, which is why the differences in the number of eggs laid on each bean type between the observed and experimental groups are statistically significant. In other words, it is highly unlikely to obtain a similar difference (or a larger one) due to chance. This allowed for concluding that the oviposition behavior of female beetles is determined by the type of bean.
Fig. 3. Chi-squared distribution (df = 5).
Figure 4 shows the observed number of adults (both males and females) hatched from eggs by bean type. There was no need to develop two different figures for the observed number of male and female adults hatched from eggs as no adults survived. The experiment did not intend to explain the survival rate of zero.
Fig. 4. The observed number of male and female adults hatched from eggs.
Butterflies
Butterflies are insects that have six jointed legs, four scale-covered wings, a three-section body, and an exoskeleton. Like all insects, butterflies have a four-stage life-cycle consisting of egg, larva, pupa, and adult. Even though butterfly wings are colorful and vibrant, they are actually transparent and made of long-chain polymer chitin. What makes them seem colorful is the light in different colors reflected by thousands of scales that cover the wings. As butterflies age, these scales fall from their wings, leaving transparent spots.
Interestingly, butterflies taste food with their feet on which taste sensors are located. To find a host plant and food, butterflies stand on a leaf, drumming it with their feet to determine if their caterpillars will be able to eat it. Only after a butterfly identified the right plant will it lay the eggs on it. Curiously enough, it has long been hypothesized that female insects lay eggs on plants that are associated with high larval performance. However, this hypothesis has not yet been proven as studies failed to find any strong link between oviposition behavior and larval performance. Despite the fact that it was expected that oviposition preferences of female butterflies maximize the fitness of their offspring, no correlation between the two factors under natural and experimental settings was found (König et al. 2781). This may be explained by the impossibility of evaluating the suitability of the plant quality since butterflies usually lay eggs on all plants of a certain kind that they encounter. Another reason for the lack of correlation is the limited amount of time in which butterflies have to lay eggs.
Work Cited
König, Malin, et al. “Butterfly Oviposition Preference Is Not Related to Larval Performance on a Polyploid Herb.” Ecology and Evolution, vol. 6, no. 9, 2016, pp. 2781–2789.
Generally, liquids expand on heating and contracts on cooling. Water is a liquid which also expands on heating and contracts on cooling. However, when water is cooled, it begins to contract until the temperature reaches 4 degree Celsius, following which it begins to expand until it reaches 0 degrees. This expansion of water as it is cooled is not usual since most substances, especially liquids contract when they are cooled. Due to this abnormal expansion of water, the density of water is greater at 4°C than when it is at 0°C. It is for this reason that ice floats on water and freezing in ponds, lakes and other water bodies occurs foremost at the surface level gradually progressing downwards, which is also the crucial reason why aquatic life survives even at extremely low temperatures.
This paper aims to experiment and analyse this anomalous expansion of water which occurs when water is cooled from a temperature of four degree Celsius to zero degree Celsius.
Procedure/apparatus 5 NOT A COOKBOOK OR SET OF INSTRUCTIONS. Give a one paragraph description of how you went about testing your hypothesis
In order to test the hypothesis that water displays unusual characteristics in its expansion and contraction, the following procedure and apparatus were utilized. Water (100ml) was boiled to a temperature of about hundred degrees in a flask and as soon as it reached its boiling point, the heat was removed and the water in the flask was measured using a measuring jar. This water was allowed to cool and after it came to room temperature, all the water in the flask (100ml) was filled in a dish and its level was marked. The dish was then placed in the ice chamber of the refrigerator. The temperature of the water was regularly measured using a thermometer as the water began to cool. It was noted that as the water began to cool from about ten degree Celsius and became substantially cooler it began to contract. This was visible by noting the drop in level of water in the flask, which had been marked. However, the temperature of the water began to reduce and when the temperature dropped to 5 degree Celsius, the research noted the change in temperature every five minutes. It was found that when the water reached a temperature of four degree Celsius, it began to expand. This was noted by the increase in volume of the water in the flask, which showed an increase, well above the level which had been marked when the experiment was initiated. The volume of water in the flask had increased substantially, rather than reducing, which showed that water had expanded on cooling as opposed to the normal tendency of most substances to contract on cooling.
Interestingly, when this frozen form of water, ice, was again removed from the refrigerator and heated in the measuring beaker, it showed a reduction in volume. Thus, when the water had been heated above a temperature of four degree Celsius, it showed a decrease in volume from its volume between zero and four degree Celsius.
Raw Data 5 Give your experimental results in the form of TABLES or GRAPHS (CHARTS). AND Describe the information in your tables & graphs.
The above graph is a clear representation of the unusual expansion of water with changes in temperature. The volume of water relative to the changes in temperature is depicted above in the graph. The curve which is formed denotes the reduction in volume of water as the temperature increases from zero degrees to about four degrees Celsius. After four degree Celsius, the curve changes its direction and moves the other way round which indicates that the volume of the water increases wit temperature once again. The experiment and the data prove that water expands anomalously when it is cooled from four degrees to zero degrees, through the increase in volume which occurs.
The data and findings support the hypothesis that between zero and four degrees, water depicts unusual properties and expands, rather than contracting, and increases in volume. The measure of water was observed during the entire process and the sudden change in the volume of water was noted. This investigation proves the hypothesis that water expands anomalously when cooled and increases in volume as it nears its freezing point of zero degree Celsius.
One of the critical activities when designing a market research experiment is the development of a reasonable hypothesis. Ideally, it can be defined as speculation pivoted on secondary data sources, such as the internet of enlisting possible research findings will be. It provides possible but unconfirmed answers to a given research topic. A breakdown of how to develop and test a market hypothesis and a justification of the testing methods form the basis of discussion for this paper.
Steps for Developing an Effective Hypothesis Statement
The first step involves identifying the research question addressed during the market experiment. Secondly, the researcher should list the expected results with each speculation beginning with ‘if’ and containing two elements namely the dependent and independent variables. Lastly, the prober should document a second hypothesis that opposes any correlation between the two parameters (Dhir & Gupta, 2021). For instance, if local authorities build more stalls, the number of customers served in the market will not be affected. The hypothesis above can be tested to establish its efficacy, as discussed below.
Testing of Hypothesis
Upon formulation, the hypothesis is evaluated based on the S.M.A.R.T. technique. This implies that a reasonable assumption should be specific, measurable, achievable, realistic, and adhere to the time limits. In a nutshell, the hypothesis should contain all variables on the targeted market group to be studied and a recap of the expected results from the experiment. The section below validates the reason for the adoption of this technique.
The Rationale for the Adoption of the S.M.A.R.T. Method
The S.M.A.R.T. theoretical rubric enables the researcher to determine the exact parameters to be explored during the experiment and identify links between study variables. Therefore, it is easier for probers to identify important elements while eliminating those that could be irrelevant. In addition, the hypothesis ensures that the question meets the criteria of clarity and appropriateness and falls within the confines of the study.
Conclusion
A reasonable hypothesis serves as a template guiding researchers towards the better realization of study objectives. It bridges the gap between experimental assumptions and actual results. In that regard, probers need to do extensive research on study topics before formulating hypothesis statements for any given market experiment. Analyzing preexisting data sources can help researchers to develop objective and testable postulations regarding the study’s actual findings.
Theoretical models imply that seed predation significantly influences population structure which translates to the entire community. As such, empirical studies carried out in a diversity of niches suggest that post-dispersal seed predation accounts for greater seed loss. Given this, a study carried out with an objective of, first, to determine the impact of seed predation on seed environment (covered and open), and second, to determine the same on the seed sizes were carried out.
The experimental design was such that four treatments were designed with each having five traits, which held clusters of 20 seeds each. Noteworthy, the experiment was carried out on an ‘observatory hill’ where an entire cluster of seeds (either sunflower or lentils) was replaced on observing a decrease in quantity. The records of the seed population were taken daily within a fixed period for five consecutive days. The data was then analyzed using Chi-square.
The analysis revealed that there was no significant difference on both occasions at 95% CI. As such, there was a negligible impact of seed predation on such scenarios. To this end, to achieve accurate results, future designs should try to eliminate the effects of artificial factors e.g. transfer of chemicals to seeds where bare hands (without gloves) are used when introducing the seeds to the site.
Introduction
Theoretical models suggest that seed predation significantly functions to structure plant population which translates to the larger community. To this end, empirical studies carried out in a diversity of niches contend that post-dispersal seed predation presents a potential cause of extensive seed loss (Howe & Miriti, 2004). Nevertheless, many other factors may hinder the seedling establishment, and as such their effects can dwarf seed predation.
For instance, with an unfavorable microsite, the effect of seed predation on plant recruitment is decimated. Moreover, excessive protection typified by most perennial plants which provide safe sites (seed banks) for seeds limits seedling establishment. To date, even with well-documented literature on the degree of predator influence on seed abundance, the link between their dynamics remains unclear. This is owed to the fact that there are limited studies done on the same even on areas where the situation seems alarming.
It is tricky to give a general statement on predators’ degree of influence on plant recruitment. This is so because of the striking and varied distinct traits displayed by seeds that don the earth. As such, the susceptibility of seeds to predators varies, for example, with size, strength, and the presence of elaiosomes. Moreover, an ecosystem may host a diverse number of predators that have different preferences thus altering plant population in a complex manner.
In synopsis, a combination of these factors results in a complex community structure. However, seed predators should not be condemned since they too are beneficial. For instance, predation could lead to seed pollination of a mature seed (post-dispersal) that presents a potential new adult in the community. Nevertheless, predation is harmful when a seed in question is premature (pre-dispersal predation) since at this stage it cannot grow even with a favorable microsite. As such, we are tempted to scrutinize the role of seed dispersal in seed recruitment.
Fundamentally, seed dispersal represents “one of the most ecologically significant plant-animal mutualisms and it is central for understanding plant population and community structure” (Bronstein, Alarcón & Gerber, 2006). Depending on the kind of interaction, the net outcome could eventuate in mutualism or antagonism. Nevertheless, the location of the seed after dispersal is what determines whether a seed would grow or not. Therefore, it follows that there are indeed sites that could support seed recruitment in a community while others would not. The microsites that would support seed recruitment (safe sites) provide protection of seeds from predators and at the same time support their growth.
In essence, seeds must be secured from discovery by predators to enhance their chances of growth. Some of the protective sites include a probable crack present in the soil that a seed might land in, a canopy of leafy plants, and a covering of trash. Nonetheless, this should enhance the ecological factors that boost seed recruitment in a community. Given seed protection, the act of dispersal may on the other handguard seeds from predators.
Seeds that find themselves by chance within a parent’s proximity are more susceptible to predator attack than those that are far away (Zwolak & Crone, 2012). In essence, these seeds would easily be spotted by predators that target the parent. Moreover, even if the seeds are dispersed far away from the parents, they might present a potential target for attack by predators when they happen in clusters. For instance, this is common in legumes that have seeds embedded within pods. If by chance the pods fail to burst in the course of dispersion then the seeds will be in clusters. Consequently, they would easily be spotted by a predator (Ordoñez, Molowny-Horas & Retana, 2006).
Noteworthy, some seeds are responsible for protecting themselves from predator attacks thanks to their peculiar nature. In essence, seed discovery and consumption are two separate things. The fact that a seed has been spotted by a predator does not mean that it is in jeopardy. Different kinds of seeds offer unique and varied modes of predator repellant traits. For instance, some seeds have a bad taste while others are toxic to predators.
To this end, we may refer to the infamous castor bean. Commonly found in our niche is the seed of ‘antelope brush’ (Purshia tridentata). This seed is extremely bitter thus deterring predator attack. Moreover, small-sized seeds might be a deterrent to predator attacks. For instance, to a vole, it is uneconomical to explore minute seeds that do not exceed a millimeter in length. This is despite their occurrence in clusters.
A vole considers it a waste of time. In a nutshell, “the size, palatability, and distribution of seed found on the soil surface in a community may greatly influence which seeds are taken by predators” (Andersen, 1989). This will in turn determine which species germinated and hence assume a large role in the ecosystem. As such, the objective of this report is divided into two: first, is to determine that there is no significant difference in seed predation between plants planted within protected (covered) and unprotected (open) environs hence our first null hypothesis (H01); second, is to determine that there is no significant difference in seed predation with regards to seed sizes hence our second null hypothesis (H02).
The experimental design was set up on an ‘observatory hill’ where the daily seed populations were monitored for analysis.
Methods
In this experiment, seeds of lentils and sunflower were placed on ‘observatory hill,’ but on known locations. The seeds were placed in clusters but along transects in a community. Noteworthy, depending on the expected questions the seeds were either placed in direct contact with the ground, or on aluminum dishes. To this end, the essence was to prevent them from being blown away by the wind. This would, however, bring in an artificial factor that could alter the final results.
The experimental design decided was such that four treatments were set composing of five traits each, and holding clusters of 20 seeds per trait. In a nutshell, 2000 seeds were used. Two treatments were meant for sunflower seeds, one for lentils, and a final one that contained both at equal ratios. The seeds populations were to be checked and recorded for each of the five consecutive days, but periodically. Fundamentally, on noticing reduced population per location in the previous census, instead of replacing the lost ones, the entire population of seeds was to be replaced by new ones. Of note, with five members per group, each student was given a chance to count and record the observation in the course of the experiment. The data obtained were then analyzed using Chi-square to test the framed hypotheses.
Results
Table 1. Chi-square analysis on the impact of seed predation on the nature of seed environment (covered or uncovered)
Sum of (Observed treatment 1-Expected treatment)^2/ (Expected treatment)
0.17153
Chi-square value
0.17153
Degree of freedom (df)
1
Alpha value
0.05
Critical test value for comparison
3.84
The test value (0.17153) at 1 degree of freedom does not exceed the critical value 3.84. Therefore, there is no significant difference between treatments because the p-value is not less than or equal to 0.05. For more information see appendix 1.
Table 2. Chi-square analysis on the impact of seed predation on the grain size.
Sum of (Observed treatment 1-Expected treatment)^2/ (Expected treatment)
0.007332
Chi-square value
0.00733
Degree of freedom
1
Alpha value
0.05
Critical test value for comparison
3.84
The test value (0.00733) at 1 degree of freedom does not exceed the critical value of 3.84. Therefore, there is no significant difference between treatments because the p-value is not less than or equal to 0.05. For more information see appendix 2.
Discussion
The objective of this experiment was partly to determine whether there exists seed predation among seeds placed in different environs (protected or unprotected), and partly to determine whether the same is true with seed sizes. The species under observation were sunflower and lentil seeds. Using Chi-square analysis (tables 1 and 2) the results of the experiment were presented.
With regards to the impact of seed predation on the nature of the seed environment, the analysis revealed that there was no significant difference between covered and uncovered seed environs at a 95% confidence interval. Basically, at 1 degree of freedom, the test value (0.17153) was below the critical value (3.84). As such, we conclude that there is no seed predation between distinct seed environs (in this case covered and open environs) in sunflower seeds. In an ideal ecosystem, the contrary would have happened since we expect seeds that are in an open environment to be more vulnerable to predation. This would happen because they are more likely to be seen than the covered ones.
However, there might have been so many other factors that might have contributed to the recorded observations. For example, there is the likelihood that sunflower seed predators were limited hence the impact was not substantial. Moreover, the seeds probably deterred predators by either chemical emissions or grain strength. Other factors that might have contributed to this observation might have emanated from the person performing the test. For instance, handling seeds with bare hands devoid of gloves might transfer predator-repellant chemicals to the seeds.
Concerning the impact of seed predation on the grain size, the analysis revealed that there was no significant difference between seed sizes (sunflower and lentils) at a 95% confidence interval. Basically, at 1 degree of freedom, the test value (0.00733) was below the critical value (3.84).
Therefore, we conclude that seed predation among seed sizes (lentils and sunflower seeds) was absent. Basically, on relying on seed sizes alone without keeping other factors constant the ensuing results would not reflect the true picture. Ideally, seed predators have diverse taste preferences which cut across grain sizes. As such, given a variety of grain species with different sizes, it is no surprise that a predator would opt for a smaller grain. In a nutshell, on the ‘observatory hill,’ there is the likelihood that there was a limited population of preferred predators (of lentils and sunflower).
However, in an ideal situation where the two samples (lentils and sunflower seeds) present equal preferences to a consumer (predator) than sunflower seeds (big size) would be the most targeted. Other factors that might have contributed to the observation above could have emanated from the nature of the seed. For example, toxics and bitterness act as predator repellants. Moreover, there could have been an artificial factor that sneaked into the system e.g. chemicals present on the hands where gloves were not used during seed placement.
In the future, to obtain accurate results then the person should be advised to use gloves when placing the seeds to minimize the introduction of an artificial factor. Also, on determining the impact of seed predation on the grain size, the design should dwell on one variety of seeds to eliminate the effect of taste preferences. Finally, the ‘observatory hill’ chosen ought to be diversified to be a representative of a natural ecosystem.
In a conclusion, the experimental design met its objective which was to partly determine whether there exists seed predation among seeds (sunflower) placed in different environs (covered or open), and partly to determine whether the same is true with seed sizes (sunflower and lentil seeds). As such, it was concluded that in both scenarios there was no significant difference in seed predation.
References
Andersen, A. (1989). How important is seed predation to recruitment in stable populations of long-lived perennials? Oecologia, 81 (1), 310–315.
Bronstein, J., Alarcón, R. & Gerber, M. (2006). The evolution of plant-insect mutualisms. New Phytol 172 (1), 412–428.
Howe, H. & Miriti, N. (2004) When seed dispersal matters. Journal of Bioscience, 54 (1), 651–660.
Ordoñez, L., Molowny-Horas, R. & Retana, J. (2006). A model of the recruitment of Pinus nigra from unburned edges after large wildfires. Ecol Model, 197 (1), 405–417.
Zwolak, R & Crone, E. (2012) Quantifying the outcome of plant-granivore interactions. Oikos Journals, 121 (1), 20–27.
Appendix
Appendix 1
Table of sunflower population grown in open/covered environment
Treatment 1 Open 20 Sunflower seeds
RAW DATA
Rep
1
2
3
4
5
Total
Day 1
Day 2
20
20
20
20
20
100
Day 3
20
20
20
19
20
99
Day 4
20
20
20
20
20
100
Day 5
20
18
20
20
20
98
Average
99.25
Treatment 2
Covered 20 Sunflower seeds
RAW DATA
Rep
1
2
3
4
5
Total
Day 1
Day 2
20
20
20
20
20
100
Day 3
18
20
20
20
20
98
Day 4
20
20
20
20
20
100
Day 5
20
19
20
17
0
76
Average
93.5
Appendix 2
Table of sunflower and lentil seeds population sharing the same area on one part, and lentil seed population placed separately on the other part.
Light travels and behaves both as a discrete bundle of photon and wave, which explains how the photoelectric effect of electrons works. The effect can be explained as the injection of electrons from the surface of any mental in response to a given wavelength of light. Rablau et al. (2019) explain that the emission of conduction electrons from metals requires voltage at a short wavelength, such as UV light (visible light).
In this experiment, the velocity of leading the metal surface is studied from a monochromatic light which proves the wavelength of light is matter and not intensity. The experiment exposes the photocathode to monochromatic radiations with a potential applied to the amplifier to oppose the electron energy emitted. The voltage required to stop the current is proportional to the energy emitted; thus, voltage data is obtained and plotted to obtain the stopping voltage allowing the current to reach zero on the meter. After obtaining the stopping voltages, a straight line plot is generated, the slope is used to obtain the Planck’s constant. Einstein energy equation (1) showed the inverse relation between energy and wavelength, which is equated to voltage and work function.
Stopping voltage where it is equal to that of the work function for the electron to be generated;
Plotting a graph against the inverse of the corresponding wavelengths;
The literature planks constant is 6.626 * 10 ^-34 J.s.
The procedure
The Photoelectric effect apparatus was set up on a small stack of books making the aperture opening be 7cm above the table to line up with the mercury lamp.
A digital voltmeter (physics multimeter) was correctly connected to photoelectric effect apparatus using a red and black patch cord. The red patch cord of the photoelectric effect apparatus was connected to the mA outlet of the voltmeter, while the black patch cord was connected to the COM outlet.
A 546 nm filter was placed in front of the aperture on the photoelectric effect apparatus. The mercury lamp was placed directly in front of the opening, and then the lamp was turned on.
The photoelectric effect apparatus was set to zero by adjusting the knop counterclockwise with the current reading near zero while the voltage readings were high. The zero knob was adjusted so that the current meter needle was zero. The voltmeter was turned to near-zero with the current meter still displaying current.
When the highest current reading was attained, the voltage readings were taken after every 0.5 amps, which were achieved by turning the voltage knob until the needle reached the correct number. At this point, voltage readings were recorded. The measurements were taken until the current dropped to zero.
The experiment was repeated while replacing the lamp and filter combination five times. An average voltage at every current reading was obtained.
The filter was changed to 436 nm with a mercury lamp, and the experiment was repeated while re-zeroing the apparatus.
Reference
Rablau, C. I., Ramabadran, U., Book, B., & Cunningham, R. (2019). The photoelectric effect: Project-based undergraduate teaching and learning optics through a modern physics experiment redesign. Fifteenth Conference on Education and Training in Optics and Photonics: ETOP 2019.
Experiment 1: Shear Force Variation with an Increasing Point Load
Calculate the theoretical shear force at the cut and complete the Table 2.
Mass (g)
Load (N)
Experimental Shear Force (N)
Theoretical Shear Force (N)
0
0
0
0
100
0.98
0.6
0.579
200
1.96
0.8
1.158
300
2.94
1.2
1.737
400
3.92
1.4
2.316
500
4.9
3.1
2.895
Calculations:
Theoretical Shear Force (N)
Sc=w*a/L
Where L=0.44m
And a=0.26m.
For 100g (0.98N)
Sc=w*a/L
Sc=0.98*0.26/0.44=0.579N
For 200g (1.96N)
Sc=w*a/L
Sc= 1.96*0.26/0.44=1.158N
For 300g (2.94N)
Sc=w*a/L
Sc=2.94*0.26/0.44 =1.737N
For 400g (3.92N)
Sc=w*a/L
Sc=3.92*0.26/0.44=2.316N
For 500g (4.9N)
Sc=w*a/L
Sc=4.9*0.26/0.44=2.895N
Plot a graph which compares your experimental results to those you calculated using the theory. Comment on the shape of the graph. What does it tell us about how shear force varies due to an increased load? Does the equation we used accurately predict the behaviour of the beam?
The shape of the graph shows a linear relationship between shear forces and the weight of material on the beam for experimental shear force. This means that for every increase in weight on the beam, there is a linear increase in shear force. However, for the experimental shear force graph, it indicates a nonlinear relationship, which creates a curved line on the graph. This could be attributed to such experimental errors as permanent deformation of the beams after long years of use. Despite the difference in shear forces between theoretical and experimental, the results do indicate that the equation used could predict the behavior of the beam, but not accurately predict it because of experimental errors.
Experiment 2: Shear Force Variation for Various Loading Conditions
Record the Digital Force Display reading in a table as in Table 3.
Figure
W1(N)
W2(N)
Experimental Shear force (N)
RA(N)
RB(N)
Theoretical Shear force (N)
4
3.92
-1.4
5.17
-1.25
-1.2
5
1.96
3.92
2.4
2.564
3.276
3.32
6
4.91
3.92
1.9
2.588
6.242
2.32
Calculate the support reactions (RA and RB) and calculate the theoretical shear force at the cut.
For Figure 4
The algebraic total of the moments produced by the forces operating to the left or right of the cut determines the bending moment at the “cut.” Therefore;
However, the product of the force being applied to the beam with respect to the cut between the fixed end of the beam and the force application point is the shear force moment.
Comment on how the results of the experiments compare with those calculated using the theory.
The results of the experiments and those of the calculated figures using the theory were. For instance, in Figure 4, the experimental shear force is -1.4N, yet using theory to calculate the shear force, a value of -1.2N was obtained. Moreover, the results were different for both Figure 5 and Figure 6. For Figure 5, the experimental shear force obtained is 2.4N, yet its theoretical shear force is 3.32N. The results are also different in Figure 6, where the experimental shear force obtained is 1.9N as compared to its theoretical shear force of 2.32N. The difference in the experimental results and those from calculated theoretical figures could be as a result of experimental errors. Such errors may include using old beams that have undergone permanent deformation, errors in reading the shear forces, or errors due to room temperature that might result in the materials making the beams expand, hence wrong results.
The Atlantic tomcod, biologically known as the Microgadus tomcod, is native to the coasts of North America and Canada, where it is popular with fishermen, especially between December and February when it is in season. It mostly lives in brackish coastal or freshwater, which makes it a common inhabitant of rivers and landlocked lakes. Its diet, for the most part, consists of crustaceans, worms and other small water creatures.
Background
What sets this species of fish apart from most other aquatic animals is the speed with which it evolved in the last half century, enabling it to survive the effects of perennial human water pollution. From the late 40s to early 90’s, the Hudson River, one of the biggest natural habitats for the tomcod, was contaminated with over a million pounds of polychlorinated biphenyls (PCBs) from industrial installations (Klauda, Thomas and Gary 831). Consequently, thousands of fish and other creatures living in these waters died, resulting in an acute natural imbalance. However, instead of dying out as expected, the tomcod begun to thrive, even in the high concentration of toxic chemicals (Dybas). Initially, they died just like other fish from a series of chemical related disorders especially heart defects in juveniles. As a result, most of the fish hatched after the contaminating event were gradually eliminated leaving only a few survivors. However, after a few decades, they appeared to have developed immunity to the effects of the poison, which motivated biologists to investigate their coping mechanism (Wirgin et al. 1322). Newly hatched fish showed no outward signs of heart defects even after the near depletion of some of the other species from the same problem. Scientists discovered that, despite their continued survival, the levels of PCBs in their blood were the highest in nature (Klauda, Thomas and Gary 833). In fact, for any other fish, even a fraction of the tomcod’s PCB blood levels would have been be lethal.
It later emerged that they underwent extremely rapid evolution, which allowed them to modify some of the proteins in their DNA and regulate the toxicity of PCBs. As a result, it became difficult for toxins to bind to their receptors, making them ineffective against the tomcod (Wirgin et al. 1323). Studies of the small fish in different rivers have shown that this mutation is mostly limited to the Hudson, although, there are some other parts of the US, such as the Niantic River in Connecticut, where a similar mutations have been observed. Scientists argue that the tomcod must have inbuilt mechanisms for mutation, which are triggered whenever they are faced with chemical pollutants (Yuan et al. 78). The process of natural selection occurs when organisms that are unable to survive in a given environment are allowed to die out, while those with suitable adaptive genes reproduce and replace them. Through this process, a species can protect itself from extinction by only retaining the fittest to breed future threat-resistant generations. The tomcods’ evolution followed the law of natural selection in that, the hatchlings of the fish without the mutating gene died. Conversely, those with the gene survived and passed it on to their young, making them immune to the toxins in the water and ultimately creating a generation of PCB resistant tomcod. As a result, with each successive generation, the non-mutant fish were “weeded” out ensuring that their genes could not contaminate the surviving fish by breeding with them. The mutation is however only useful for the tomcod’s survival since, when other animals feed on them they suffer the ill effects of PCBs (Dybas). I chose to study these organisms since the mutations they employ to deal with chemical toxins are similar to the adaptations of various human and animal bacteria to antibiotic treatment. Therefore, understanding the evolution of the tomcod may be useful in discovering new knowledge in pharmacy and medicine, especially concerning antibiotic-resistant organisms. In addition, they provide a unique opportunity to watch the process of evolution as it happens since it typically takes thousands of years for animals to develop such mutations. Although some critics have opposed the application of Darwinism to tomcod evolution, their changes, are nonetheless, directly connected to the process of natural selection. The rapid evolution of tomcod can also be compared to another, “live” evolution process involving the African elephants, which are losing their tusks, apparently, to protect themselves from poachers. The tomcod, therefore, provides one with invaluable insight in the field of evolution by acting as an accessible, verifiable example.
Hypothesis
While the tomcod’s adaptation has been found to be very useful in surviving toxic waters, it has been suggested that these evolutionary traits, by virtue of their rapid nature may not be sustainable. If the Hudson mutants were relocated to an environment where they are not exposed to PCBs, they might not survive and would need to mutate once again to adjust to clean water. The rapid evolution, according to some scholars is only a temporary measure in response to a temporary problem. Therefore, when the river is clear of toxic waste in the future, they will likewise readjust their DNA through natural selection. The fish that need PCBs to survive, as a result of the initial mutation will die out, and “normal” tomcod will once again populate the Hudson River. The hypothesis for the experiment outlined later in this paper will address the ability of the tomcod’s rapid evolution to reoccur in a situation where the factors that sparked it off are absent. Therefore, I propose that, if tomcod were removed from the Hudson River, or if the river eventually became chemical free, it would have the same effect poisoning the river had on original non-mutant fish.
The Experiment
Testing this hypothesis will require recreating conditions similar to the ones that would occur if Hudson tomcod were forced to live in PCB free water. To do this, I would first obtain samples of the fish from the Hudson River and introduce them into a PCB free water system, in either a fishpond or a restricted part of their natural habitat for observation. As a control experiment, I would also move the same number of fish from the Hudson to the Niantic where similar mutations have been observed. Once again, this would be in a controlled system to allow tracking and observation. I would then study samples in both areas, following their gestation periods and collecting data on how they react and adapt to the new environments. Since the original mutations took place over several decades, I would ensure I have introduced enough fish in the new areas to create space for natural selection. With each new generation of fish, I would collect some samples and test them to find out if the mutations that allowed them to survive the PCB poisoning are still present and if they are undergoing any genetic change. The samples collected will taken to a laboratory for dissection and biopsies will be collected from their tail fins, where evidence of PCB poisoning is most obvious due to accumulation. For these tests, the DNA from the samples will be isolated to determine the presence and levels of PCB related mutations. The process will require a QUIAGEN DNA kit to purify the DNA material collected and then using alcohol precipitation, I will form DNA pellets and re-suspend them in a Hydration solution. I will then read the different samples using a Nanodrop spectrophotometer to establish the PCB levels and the purity of the DNA. The data collected from the tomcod in the “clean” river and the control setting will be used to create a statistical analysis, which will guide me in making various inferences on the effects of PCB free water on their rapid evolution. I will also record statistics on the fish population and their mortality rates with special emphasis on anything associated with heart or liver problems, since these are the main manifestations of PCB poisoning. I would then use the information gathered to establish the extent of the changes occurring in both environments and determine if indeed Hudson River Atlantic tomcod can survive in clean water without backward evolution. If they do, it will mean they have successfully evolved sufficiently to live in both polluted and unpolluted environments, a fact that most scientists involved in their study have dismissed as unlikely.
Works Cited
Dybas, Cheryl L. “Toxic Waters Provide a Snapshot of Evolution.” The Washington Post 23 Jan. 2006. Web. 4 March. 2015.
Klauda, Ronald J., Thomas H. Peck, and Gary K. Rice. “Accumulation of polychlorinated biphenyls in Atlantic tomcod (Microgadus tomcod) collected from the Hudson River estuary, New York.” Bulletin of environmental contamination and toxicology 27.1 (1981): 829-835. Print.
Wirgin, Isaac, Nirmal K. Roy, Matthew Loftus, R. Christopher Chambers, Diana G. Franks, and Mark E. Hahn. “Mechanistic basis of resistance to PCBs in Atlantic tomcod from the Hudson River.” Science 331, no. 6022 (2011): 1322-1325. Print.
Yuan, Zhanpeng, et al. “Evidence of spatially extensive resistance to PCBs in an anadromous fish of the Hudson River.” Environmental health perspectives (2006): 77-84. Print.