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10: Environmental Factors - Biology


Competition is fierce out in the microbial world (non-microbial world, too!) and resources can be scarce. For those microbes that are willing and able to adapt to what might be considered a harsh environment, it can certainly mean less competition. So what environmental conditions can affect microbial growth? Temperature, oxygen, pH, water activity, pressure, radiation, lack of nutrients…these are the primary ones. We will cover more about metabolism (i.e. what type of food can they eat?) later, so let us focus now on the physical characteristics of the environment and the adaptations of microbes.

Osmolarity

Cells are subject to changes in osmotic pressure, due to the fact that the plasma membrane is freely permeable to water (a process known as passive diffusion). Water will generally move in the direction necessary to try and equilibrate the cell’s solute concentration to the solute concentration of the surrounding environment. If the solute concentration of the environment is lower than the solute concentration found inside the cell, the environment is said to be hypotonic. In this situation water will pass into the cell, causing the cell to swell and increasing internal pressure. If the situation is not rectified then the cell will eventually burst from lysis of the plasma membrane. Conversely, if the solute concentration of the environment is higher than the solute concentration found inside the cell, the environment is said to be hypertonic. In this situation water will leave the cell, causing the cell to dehydrate. Extended periods of dehydration will cause permanent damage to the plasma membrane.

Hypertonic vs. Hypotonic Solutions.

Cells in a hypotonic solution need to reduce the osmotic concentration of their cytoplasm. Sometimes cells can use inclusions to chemically change their solutes, reducing molarity. In a real pinch they can utilize what are known as mechanosensitive (MS) channels, located in their plasma membrane. MS channels open as the plasma membrane stretches due to the increased pressure, allowing solutes to leave the cell and thus lowering the osmotic pressure.

Cells in a hypertonic solution needing to increase the osmotic concentration of their cytoplasm can take up additional solutes from the environment. However, cells have to be careful about what solutes they take up, since some solutes can interfere with cellular function and metabolism. Cells need to take up compatible solutes, such as sugars or amino acids, which typically will not interfere with cellular processes.

There are some microbes that have evolved to extreme hypertonic environments, specifically high salt concentrations, to the point where they now require the presence of high levels of sodium chloride to grow. Halophiles, which require a NaCl concentration above 0.2 M, take in both potassium and chloride ions as a way to offset the effects of the hypertonic environment that they live in. Their evolution has been so complete that their cellular components (ribosomes, enzymes, transport proteins, cell wall, plasma membrane) now require the presence of high concentrations of both potassium and chloride to function.

PH

pH is defined as the negative logarithm of the hydrogen ion concentration of a solution, expressed in molarity. The pH scale ranges from 0 to 14, with 0 representing an extremely acidic solution (1.0 M H+) and 14 representing an extremely alkaline solution (1.0 x 10-14 M H+). Each pH units represents a tenfold change in hydrogen ion concentration, meaning a solution with a pH of 3 is 10x more acidic than a solution with a pH of 4.

Typically cells would prefer a pH that is similar to their internal environment, with cytoplasm having a pH of 7.2. That means that most microbes are neutrophiles (“neutral lovers”), preferring a pH in the range of 5.5 to 8.0. There are some microbes, however, that have evolved to live in the extreme pH environments.

Acidophiles (“acid lovers”), preferring an environmental pH range of 0 to 5.5, must use a variety of mechanisms to maintain their internal pH in an acceptable range and preserve the stability of their plasma membrane. These organisms transport cations (such as potassium ions) into the cell, thus decreasing H+ movement into the cell. They also utilize proton pumps that actively pump H+ out.

Alkaliphiles (“alkaline lovers”), preferring an environmental pH range of 8.0 to 11.5, must pump protons in, in order to maintain the pH of their cytoplasm. They typically employ antiporters, which pump protons in and sodium ions out.

pH Scale. OpenStax, Inorganic Compounds Essential to Human Functioning. OpenStax CNX. Jun 18, 2013 http://cnx.org/contents/[email protected]

Temperature

Microbes have no way to regulate their internal temperature so they must evolve adaptations for the environment they would like to live in. Changes in temperature have the biggest effect on enzymes and their activity, with an optimal temperature that leads to the fastest metabolism and resulting growth rate. Temperatures below optimal will lead to a decrease in enzyme activity and slower metabolism, while higher temperatures can actually denature proteins such as enzymes and carrier proteins, leading to cell death. As a result, microbes have a growth curve in relation to temperature with an optimal temperature at which growth rate peaks, as well as minimum and maximum temperatures where growth continues but is not as robust. For a bacterium the growth range is typically around 30 degrees.

The psychrophiles are the cold lovers, with an optimum of 15oC or lower and a growth range of -20oC to 20oC. Most of these microbes are found in the oceans, where the temperature is often 5oC or colder. They can also be found in the Arctic and the Antarctic, living in ice wherever they can find pockets of liquid water. Adaptation to the cold required evolution of specific proteins, particularly enzymes, that can still function in low temperatures. In addition, it also required modification to the plasma membrane to keep it semifluid. Psychrophiles have an increased amount of unsaturated and shorter-chain fatty acids. Lastly, psychrophiles produce cryoprotectants, special proteins or sugars that prevent the development of ice crystals that might damage the cell. Psychrotophs or cold tolerant microbes have a range of 0-35oC, with an optimum of 16oC or higher.

Humans are best acquainted with the mesophiles, microbes with a growth optima of 37oC and a range of 20-45oC. Almost all of the human microflora fall into this category, as well as almost all human pathogens. The mesophiles occupy the same environments that humans do, in terms of foods that we eat, surfaces that we touch, and water that we drink and swim in.

On the warmer end of the spectrum is where we find the thermophiles(“heat lovers”), the microbes that like high temperatures. Thermophiles typically have a range of 45-80oC, and a growth optimum of 60oC. There are also the hyperthermophiles, those microbes that like things extra spicy. These microbes have a growth optima of 88-106oC, a minimum of 65oC and a maximum of 120oC. Both the thermophiles and the hyperthermophiles require specialized heat-stable enzymes that are resistant to denaturation and unfolding, partly due to the presence of protective proteins known as chaperone proteins. The plasma membrane of these organisms contains more saturated fatty acids, with increased melting points.


Growth Curves.

Oxygen Concentration

The oxygen requirement of an organism relates to the type of metabolism that it is using. Energy generation is tied to the movement of electrons through the electron transport chain (ETC), where the final electron acceptor can be oxygen or a non-oxygen molecule.

Organisms that use oxygen as the final electron acceptor are engaging in aerobic respiration for their metabolism. If they require the presence of atmospheric oxygen (20%) for their metabolism then they are referred to as obligate aerobes. Microaerophiles require oxygen, but at a lower level than normal atmospheric levels – they only grow at levels of 2-10%.

Organisms that can grow in the absence of oxygen are referred to as anaerobes, with several different categories existing. The facultative anaerobes are the most versatile, being able to grow in the presence or absence of oxygen by switching their metabolism to match their environment. They would prefer to grow in the presence of oxygen, however, since aerobic respiration generates the largest amount of energy and allows for faster growth. Aerotolerant anaerobes can also grow in the presence or absence of oxygen, exhibiting no preference. Obligate anaerobes can only grow in the absence of oxygen and find an oxygenated environment to be toxic.

While the use of oxygen is dictated by the organism’s metabolism, the ability to live in an oxygenated environment (regardless of whether it is used by the organism or not) is dictated by the presence/absence of several enzymes. Oxygen by-products (called reactive oxygen species or ROS) can be toxic to cells, even to the cells using aerobic respiration. Enzymes that can offer some protection from ROS include superoxide dismutase (SOD), which breaks down superoxide radicals, and catalase, which breaks down hydrogen peroxide. Obligate anaerobes lack both enzymes, leaving them little or no protection against ROS.

Oxygen and Bacterial Growth.

Pressure

The vast majority of microbes, living on land or water surface, are exposed to a pressure of approximately 1 atmosphere. But there are microbes that live on the bottom of the ocean, where the hydrostatic pressure can reach 600-1,000 atm. These microbes are the barophiles(“pressure lovers”), microbes that have adapted to prefer and even require the high pressures. These microbes have increased unsaturated fatty acids in their plasma membrane, as well as shortened fatty acid tails.

Radiation

All cells are susceptible to the damage cause by radiation, which adversely affects DNA with its short wavelength and high energy. Ionizing radiation, such as x-rays and gamma rays, causes mutations and destruction of the cell’s DNA. While bacterial endospores are extremely resistant to the harmful effects of ionizing radiation, vegetative cells were thought to be quite susceptible to its impact. That is, until the discovery of Deinococcus radiodurans, a bacterium capable of completely reassembling its DNA after exposure to massive doses of radiation.

Ultraviolet (UV) radiation also causes damage to DNA, by attaching thymine bases that are next to one another on the DNA strand. These thymine dimers inhibit DNA replication and transcription. Microbes typically have DNA repair mechanisms that allow them to repair limited damage, such as the enzyme photolyase that splits apart thymine dimers.

Key Words

osmotic pressure, passive diffusion, solute, hypotonic, hypertonic, mechanosensitive (MS) channel, compatible solute, halophile, pH, neutrophile, acidophile, alkaliphile, optimum temperature, minimum temperature, maximum temperature, psychrophile, psychrotroph, mesophile, thermophile, hyperthermophile, chaperone protein, electron transport chain (ETC), aerobic respiration, obligate aerobe, microaerophile, anaerobe, facultative anaerobe, aerotolerant anaerobe, obligate anaerobe, reactive oxygen species (ROS), superoxide dismutase (SOD), catalase, barophile, ionizing radiation, Deinococcus radiodurans, ultraviolet (UV) radiation, thymine dimmers, photolyase.

Essential Questions/Objectives

  1. What are all the descriptive terms used for microbes that live in different environments or the terms used for the environments that they live in? What does each term mean? In what types of environments are each microbial group found?
  2. What effect does solute concentration have on microbes? How can cells adapt when going from a low solute to a high solute environment and vice versa? What is a compatible solute? What microbial groups have a requirement for high solute concentrations? How do microbes differ in their response to water activity?
  3. How do microbes differ in their response to pH? What does pH affect in a cell and what do cells that live at high or low pH have to do to survive these conditions?
  4. How do microbes differ in their response to temperatures? What terms are used for these responses? If a cell is to grow at low or high temperatures, what adaptations does it need to make?
  5. How do microbes differ in their response to oxygen levels? Why would they differ? What enzymes are needed to adapt to environments containing differing amounts of oxygen?
  6. How do microbes respond to high pressure? To ionizing radiation? To UV light? What populations are resistant to these conditions?

Exploratory Questions (OPTIONAL)

  1. What is the largest bacterium or archaean ever discovered? What is the smallest eukaryote ever discovered?

How Environmental Factors Impact Mental Health

Ranked as one of the leading causes of illness and disability around the globe, mental illness is a widespread health challenge. In fact, data from the World Health Organization reveals that approximately one in four people worldwide will suffer from a mental illness at some point in their lives. While there are still plenty of researchers and clinical psychologists alike who don’t know about mental illness, one thing is for sure: these conditions are complex and multi-causal. Many people often assume mental illness simply runs in families. This is true, but genetics are only a part of it. These disorders actually occur due to a combination of factors, including a person’s environment and lifestyle.

The world a person lives and functions within can play a major role in mental health. Below, we’ll talk about two types of environmental factors that can make a person susceptible to mental illness.

Physical Environmental Factors

Physical environmental factors contributing to mental illness are those that have the power to affect a person’s biology or neurochemistry, thereby increasing their chances of developing a disorder. For example, if a person lacks access to health-related resources such as whole, nutrient-rich foods and they tend to eat more processed and refined foods, their body (and brain) won’t function optimally. As a result, if they encounter a major stressor, they may not have the resources to effectively cope.

In addition to poor nutrition, some other examples of physical environmental factors are:

  • Sleep deprivation
  • Smoking
  • Substance abuse
  • Pollution
  • Exposure to toxins during childhood
  • Extreme weather conditions (such as excessive rain or snow)
  • Hazardous conditions at work

Social Environmental Factors

Social environmental factors refer to socioeconomic, racial and ethnic, and relational conditions that may influence a person’s ability to cope with stress. A good example is not having a strong social support system. Let’s say a person loses their job or goes through a divorce. Having supportive friends and family during this time is vital to their ability to cope with the stress.

A lack of social support is just one type of social environmental factor. Others include:

  • Social stigma (such as coming out as gay or lesbian)
  • History of abuse
  • Family discord during childhood
  • Early loss of a parent
  • Poverty
  • Lack of spirituality or religious affiliation
  • Lack of meaningful work or hobbies
  • Toxic relationships
  • Lack of self-care and/or relaxation

Overall health and well-being require a good balance of mental, physical, social, emotional, and spiritual health. Although mental illness itself is heritable, a wide variety of factors like genetics, economic, social, and physical influences also contribute to the development of a disorder. All of these factors must be taken into consideration for a psychologist to effectively diagnose and treat mental illness.

Are you interested in helping others face their problems? Alliant International University offers two doctorate programs in Clinical Psychology (PsyD and PhD) that will provide the tools you need to be a rock of support for your patients. Learn about our doctoral programs. For more information, contact Alliant today.


Sources of Air Pollution

Stationary and Area Sources

A stationary source of air pollution refers to an emission source that does not move, also known as a point source. Stationary sources include factories, power plants, and dry cleaners. The term area source is used to describe many small sources of air pollution located together whose individual emissions may be below thresholds of concern, but whose collective emissions can be significant. Residential wood burners are a good example of a small source, but when combined with many other small sources, they can contribute to local and regional air pollution levels. Area sources can also be thought of as non-point sources, such as construction of housing developments, dry lake beds, and landfills.

Mobile Sources

A mobile source of air pollution refers to a source that is capable of moving under its own power. In general, mobile sources imply “on-road” transportation, which includes vehicles such as cars, sport utility vehicles, and buses. In addition, there is also a “non-road” or “off-road” category that includes gas-powered lawn tools and mowers, farm and construction equipment, recreational vehicles, boats, planes, and trains.

Agricultural Sources

Agricultural sources arise from operations that raise animals and grow crops, which can generate emissions of gases and particulate matter. For example, animals confined to a barn or restricted area produce large amounts of manure. Manure emits various gases, particularly ammonia into the air. This ammonia can be emitted from the animal houses, manure storage areas, or from the land after the manure is applied. In crop production, the misapplication of fertilizers, herbicides, and pesticides can potentially result in aerial drift of these materials and harm may be caused.

Natural Sources

Unlike the above mentioned sources of air pollution, air pollution caused by natural sources is not caused by people or their activities. An erupting volcano emits particulate matter and gases, forest and prairie fires can emit large quantities of “pollutants”, dust storms can create large amounts of particulate matter, and plants and trees naturally emit volatile organic compounds which can form aerosols that can cause a natural blue haze. Wild animals in their natural habitat are also considered natural sources of “pollution”.


Climate change affects everyone

Our lives are connected to the climate. Human societies have adapted to the relatively stable climate we have enjoyed since the last ice age which ended several thousand years ago. A warming climate will bring changes that can affect our water supplies, agriculture, power and transportation systems, the natural environment, and even our own health and safety.

Carbon dioxide can stay in the atmosphere for nearly a century, so Earth will continue to warm in the coming decades. The warmer it gets, the greater the risk for more severe changes to the climate and Earth’s system. Although it’s difficult to predict the exact impacts of climate change, what’s clear is that the climate we are accustomed to is no longer a reliable guide for what to expect in the future.

We can reduce the risks we will face from climate change. By making choices that reduce greenhouse gas pollution, and preparing for the changes that are already underway, we can reduce risks from climate change. Our decisions today will shape the world our children and grandchildren will live in.

You Can Take Action

You can take steps at home, on the road, and in your office to reduce greenhouse gas emissions and the risks associated with climate change. Many of these steps can save you money. Some, such as walking or biking to work, can even improve your health! You can also get involved on a local or state level to support energy efficiency, clean energy programs, or other climate programs.

Suggested Supplementary Reading

This website by NASA provides a multi-media smorgasbord of engaging content. Learn about climate change using data collected by NASA satellites and more.

Attribution

Essentials of Environmental Science by Kamala Doršner is licensed under CC BY 4.0. Modified from the original by Matthew R. Fisher.


Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.4796943.

Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

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Gender identity: Biology or environment?

There is strong evidence that sexual orientation is largely tied to biology and that initial gender assignment is the strongest predictor of gender identity in the case of intersex children. Researchers have yet to precisely pinpoint the etiology of transsexualism, however. Various studies suggest that both biological and environmental variables may play a role in transgender development, says Eric Vilain, MD, PhD, chief of the division of medical genetics and professor of human genetics, pediatrics and urology at the University of California, Los Angeles.

In 1999, scientists identified anatomic brain differences between transsexuals and nontranssexuals (Journal of Psychosomatic Research). More recently, Vilain and his colleagues determined that genetics may have a mild to moderate effect on transgender development (Biological Psychiatry, 2009).

The biological evidence to date is not that strong, though, says Vilain. He points to another study in the April 2010 issue of the International Journal of Andrology showing that fetal exposure to a particular chemical appeared to have an effect on brain development that is linked to gender role behavior. It's quite possible that being transgender stems from a combination of genetic and environmental factors, Vilain concludes.


Results

Phenotypic Results

Differential Stability and Change

Stability coefficients are reported in Table 1 for the three intervals under investigation (17 to 24, 24 to 29, and 17 to 29). These results were consistent with previous studies in suggesting substantial differential stability in personality traits in general, with 12-year correlations ranging from .49 to .57. More change occurred during the first wave of the study (range = .52�) relative to the second (.74–.78), consistent with the cumulative continuity principle of personality development. Overall, the magnitude of differential stability was similar across traits.

Table 1

Stability and Change in Negative Emotionality, Agentic and Communal Positive Emotionality, and Constraint during the Transition to Adulthood.

Differential CorrelationsAbsolute
Mean (S.D.)Cohen’s dF
Trait17�24�17�17242917�24�17�
NEM.53.74.4950.00
(10.00)
42.30
(10.23)
41.01
(10.05)
−.77−.13−.90550.79*
PEM-A.59.77.5850.00
(10.00)
50.75
(9.58)
50.43
(9.77)
.09.03.1216.91*
PEM-C.52.74.5250.00
(10.00)
48.97
(9.91)
48.31
(9.68)
−.11−.07−.1711.96*
CON.62.78.5650.00
(10.00)
54.95
(9.67)
56.62
(9.59)
.52.17.69322.02*

Note. NEM = Negative Emotionality PEM-A = Agentic Positive Emotionality PEM-C = Communal Positive Emotionality CON = Constraint. Trait scores were standardized on a T-scale (mean = 50, S.D. = 10) based on wave 1 data to facilitate interpretation.

Absolute Stability and Change

Observed changes in the absolute levels of traits ( Table 1 ) were generally consistent with the maturity principle of personality development. Figure 3 plots changes in all four traits over the course of the study, with trait values standardized at the baseline assessment. NEM declined substantially in the first interval and more modestly in the second. CON tended to increase with changes again being more dramatic in the first than second interval. Agentic and Communal Aspects of PEM increased slightly but the trajectories of these aspects of PEM were somewhat different: whereas Agentic PEM increased very modestly across both intervals, Communal PEM increased during the first interval and decreased very modestly during the second. The overall results, however, suggest that much more developmental change occurs for NEM and CON relative to PEM-linked traits.

Note. Trait scores were standardized at the first wave using a T-score metric.

Greater individual-level change also occurred on NEM and CON. Individual-level variability can be conceptualized as the number of individuals who reliably change on a given variable over time (Jacobsen & Truax, 1991). Using the short-term retest coefficients provided by Tellegen and Waller (2008 i.e., .89 for all traits), 50% of the sample reliably (i.e., > 2 standard errors) changed on NEM over the course of the study, 43% changed on CON, 34% changed on PEM-A, and 36% changed on PEM-C. However, these results also suggest that, while most of the changes on NEM and CON were in a similar direction (46 out of 50% of those who changed on NEM showed decreases 38 out of 43% showed increases on CON), change in PEM was the result of both individuals who increased and those that decreased on PEM-A (18% increased and 16% decreased) and PEM-C (13% increased and 23% decreased). Thus, the direction of individual change was more uniform for NEM and CON.

Growth curve modeling allows more specific inferences regarding absolute change in these traits. Models for PEM-A or PEM-C were not interpreted because of negative variances (i.e., ‘Heywood cases’). Given that absolute change was not impressive in a descriptive sense, no further efforts were made to modify these models to obtain an admissible solution, and the origins of change were not pursued for these personality domains. The CON and NEM models were saturated after correcting the fit statistics for twin data (see Kashy et al., 2008 Kenny & Olson, 2006) and thus no fit statistics are reported. Parameters from these models are given in Table 2 , and text explaining what these and other key parameters signify can be found in the Table 2 note as well as the notes for the other tables. The paths from the slope factor to the second measurement occasion were substantially greater than .58, consistent with descriptive results in suggesting that most of the change in NEM and CON occurred between adolescence and emerging adulthood, and that the rate of change declined from emerging to young adulthood. Both slope and intercept means and variances were statistically significant, again pointing to the existence of meaningful inter-individual variability in trait levels and change trajectories for both of these traits. The correlations between slopes and intercepts were negative for both traits, and only significant for CON.

Table 2

Univariate Growth Curve Parameters for Models Depicting Absolute Stability and Change in Negative Emotionality and Constraint during the Transition to Adulthood.

TraitInterceptSlopeTime 2
Slope path
coefficient
Slope –
intercept
correlation
MeanVarianceMeanVariance
Negative Emotionality50.11 * 70.97 * 𢄩.18 * 51.43 * .84 * −.34 *
Constraint49.94 * 75.13 * 6.78 * 47.88 * .72 * −.35 *

Note. Agentic and Communal Positive Emotionality growth models did not fit the data because of negative variances. Data were standardized in a T-score metric using wave 1 data. Intercept means reflect estimates of these standardized scores. Significant intercept variances indicate that these means varied across participants. Slope means show that these traits significantly changed over the three waves, and slope variances show that there was variability across participants in terms of these changes. The time 2 slope path coefficient was estimated if change were linear a value of .58 (7 years/12 years) would be expected. These values were both > .58 suggesting that more change occurred in the first relative to the second wave of the study. Significant slope-intercept correlations suggest that wave 1 scores were predictive of the magnitude of change observed over time.

Biometric Results

Fit statistics for the biometric Cholesky (to test influences on differential stability and change) and latent growth curve (to test influences on absolute stability and change) models are presented in Table 3 . We initially estimated variances, covariances, and means for the raw data to get a baseline index of fit for each trait. The Cholesky and latent growth curve biometric models were them compared to the baseline model to yield a χ 2 goodness of fit test, which is then converted to AIC. All models fit their respective data well.

Table 3

Fit Statistics for Cholesky (to assess influences on differential stability and change) and Growth Curve (to assess influences on absolute stability and change) biometric models.

Personality TraitModel𢄢lnLdfX² (𹓟)AIC
Negative EmotionalityBaseline24273.263117------
Cholesky24310.44315037.18 (33)�.82
Growth Curve24310.48314737.22 (30)�.78
ConstraintBaseline24854.863117------
Cholesky24881.21315026.35 (33)�.65
Growth Curve24880.09314725.23 (30)�.77
Positive Emotionality - AgenticBaseline24326.693146------
Cholesky24372.97317946.28 (33)�.72
Growth Curve-----------
Positive Emotionality -
Communion
Baseline24549.543145------

Cholesky24590.67317841.13 (33)�.87
Growth Curve-----------

Differential Stability and Change

Parameter estimates for the Cholesky models are presented in Table 4 , separately for each trait. There was evidence of significant genetic contributions to all four traits (accounting for 33�% of the variance), as well as significant non-shared environmental influences (accounting for 42�% of the variance). There was no evidence of significant shared environmental influence across any trait. These proportions of variance were essentially invariant across age, with little to no differences observed across the three assessments. These results suggest that personality is as heritable in late adolescence as it is in young adulthood.

Table 4

Standardized parameter estimates from the Biometric Cholesky Decomposition Model.

TraitComponent
of variance
%
Age 17
%
Age 24
%
Age 29
r17�r17�r24�
Negative
Emotionality
A.34*.33*.33*.75*
(.32, 1.0)
.86*
(.47, 1.0)
.99*
(.77, 1.0)
C.05.09.10------
E.61*.58*.57*.36*
(26, .44)
.32*
(23, .40)
.60*
(54, .66)
ConstraintA.53*.56*.49*.81*
(67, .98)
72*
(58, .88)
.96*
(86, 1.0)
C.02.01.01------
E.44*.42*.50*.44*
(35, .51)
.38*
(28, .46)
65*
(58, .70)
Positive
Emotionality -
Agentic
A.50*.50*.53*79*
(67, .93)
.73*
(62, .88)
.96*
(91, 1.0)
C.00.00.00------
E.50*.50*.47*39*
(30, .48)
.42*
(34, .50)
.58*
(51, .65)
Positive
Emotionality -
Communion
A.38*.46*.42*66*
(29, .85)
.69*
(32, .91)
.95*
(82, 1.0)
C.04.02.06------
E.58*.51*.52*.37*
(.27, .46)
.35*
(.26, .43)
.56*
(.48, .63)

Note. A, C, and E represent genetic, shared, and non-shared environmental influences, respectively. Univariate variance estimates are presented for each age in columns 3𠄵. r17�, r17�, and r24� index the genetic and environmental correlations across ages 17 and 24, 17 and 29, and 24 and 29, respectively. Shared environmental correlations are not presented since they were uniformly nonsignificant (consistent with the non-significant amounts of variance in personality accounted for by shared environmental influences at all ages) and their confidence intervals ranged from 𢄡.0 to 1.0.

As indicated by the generally non-overlapping 95% confidence intervals for the genetic and non-shared environmental correlations (see Table 5 ), genetic influences appeared to be more stable over time than were non-shared environmental influences across all personality factors. More importantly, however, the non-shared environmental correlations generally appeared to increase with age. In particular, the non-shared environmental correlations from ages 17 to 24 were significantly smaller (as evidenced by non-overlapping confidence intervals) than were those from ages 24 to 29, suggesting increased stability in the environmental effects associated with personality stability following emerging adulthood. The same general pattern of increasing etiological stability with age was also present for genetic effects, however, these differences were less pronounced (perhaps reflecting the rather high levels of genetic stability in general). In any case, such findings serve both to highlight strong genetic contributions to the differential stability of personality from late-adolescence through young adulthood and suggest that these influences become particularly stable following emerging adulthood.

Table 5

Biometric Latent Growth Curve Model results.

ACETotal
Variance
Factors %
Negative Emotionality
    򠾬tors
       Intercept (e.g., ai).455 * .013.532 * 121.56--
       Slope (e.g., as).122.086.792 * 85.01--
       Genetic/environmental
      ਌orrelations (e.g., rA)
−.11--−.46�.81--
     Residuals
      ਊge 17 (e.g., a1).082.000.246 * --67%
      ਊge 24 (e.g., a2).029.000.222 * --75%
      ਊge 29 (e.g., a3).000.017.155 * --83%
Constraint
    򠾬tors
       Intercept (e.g., ai).677 * .000.323 * 198.43--
       Slope (e.g., as).504 * .000.496 * 120.49--
       Genetic/environmental
      ਌orrelations (e.g., rA)
−.40--−.32�.63--
     Residuals
      ਊge 17 (e.g., a1).048.000.197 * --75%
      ਊge 24 (e.g., a2).049.027.124 * --80%
      ਊge 29 (e.g., a3).000.000.121 * --88%

Note. A, C, and E represent proportions of genetic, shared, and non-shared environmental influences, respectively. The intercept factor is composed of the variance that is common or stable across time. The slope factor captures systematic change over time. Both factors were decomposed into their genetic and environmental components, and therefore each row sums to 100% of the variance within that factor. Genetic and environmental correlations between factors are also indicated (none were statistically significant). The residual estimates index the variance remaining at each assessment after accounting for that contributed by the factors. Accordingly, the variance contributed by the factors is necessary for the rows to sum to 100%.

Absolute Stability and Change

Results from the biometric latent growth curve models are presented in Table 5 . As seen there, the intercept factor for NEM was significantly influenced by both genetic and non-shared environmental forces. The shared environment contribution was not significantly different from zero. The slope factor for NEM, by contrast, was influenced primarily by non-shared environmental influences. Moreover, these influences appeared to differ from those contained in the slope, as evidenced by the rather small non-shared environmental correlation between the two factors that were non-significant. The contributions of genetic and shared environmental influences to the slope were small and were not statistically significant. Finally, the residuals were relatively small and were solely non-shared environmental in origin. As measurement error will also be contained within the non-shared environmental residuals, such findings may or may not imply that there are assessment-specific non-shared environmental influences that meaningfully contribute to changes in personality over time. This cautious interpretation is augmented by the rather large amount of phenotypic variance accounted for by the latent intercept and slope factors (67�% of the variance in NEM). All in all, it appears that there are unique environmental experiences that differ across the twin siblings and which meaningfully influence absolute changes in NEM over time.

The pattern of stability and change in CON was somewhat different. The intercept factor was primarily genetic in origin (68%), although non-shared environmental influences also contributed (32%). Moreover, the slope factor was both genetic and non-shared environmental in origin. Such findings suggest that genetic influences play an important role in explaining absolute changes in CON that are associated with age. As before, these interpretations are augmented by the prominent amount of phenotypic variance in CON that is collectively accounted for by the latent intercept and slope factors (75�% of the variance at each age).


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2 METHODS

2.1 Ethics statement

This study was approved by the European Association of Zoos and Aquaria Giraffe EEP (EAZA Ex situ Program) and each participating zoo. To avoid possible disturbances from observers during the night, we only videotaped giraffe behavior. This study was noninvasive as it was observational in nature and caused no undue harm to the giraffe.

2.2 Behavior states

To analyze the nocturnal activity budget of giraffe, the overall observed behaviors were divided into five main behavior states, namely feeding, walking, standing, lying, and REM sleep. Behaviors occurring while the animal was on its four legs such as feeding, walking or standing were defined as standing activities. An animal resting on the ground was considered lying or in REM sleep position. A feeding animal was observed to be standing while browsing and ingesting concentrates or while drinking. A standing animal was recorded when the giraffe stood on its four legs without moving forward, in contrast to a walking giraffe which was observed when the animal was moving in one direction (Seeber et al., 2012 ). Rumination could be observed while a giraffe was walking, standing or lying. Nevertheless, continuous observation of rumination was not possible due to the quality of the video footage. The same applies to stereotypical behavior, which is why these behavior patterns were not included in the analyses. A lying animal was observed to be sitting on the ground with the abdomen or flank folded under and slightly displaced to the side, and the neck and head erect or slightly bent (Seeber et al., 2012 ). REM sleep was recorded when an animal lay on the ground, bent its neck backwards and rested the head on the flank/ground (Burger, Hartig, et al., 2020 Seeber et al., 2012 Sicks, 2012 Takagi et al., 2019 Tobler & Schwierin, 1996 ).

2.3 Data collection and observation period

The study was carried out in 13 EAZA zoos in Germany and the Netherlands during winter seasons 2015–2018. Participating zoos were: Burger's-Zoo Arnhem, Cologne Zoo, Duisburg Zoo, Erlebniszoo Hannover, Frankfurt Zoo, Münster Zoo, Opel-Zoo Kronberg, Osnabrück Zoo, Schwerin Zoo, Tierpark Berlin, Tierpark Hagenbeck Hamburg, Tiergarten Nuremberg, and Zoom Erlebniswelt Gelsenkirchen. Behavioral data were collected from 63 giraffe (Giraffa camelopardalis rothschildi and Giraffa camelopardalis reticulata) of all ages from seven months to 29 years. To obtain information about the nocturnal behavior of giraffe, infrared-sensitive cameras (Mobotix AllRound Dual M15) were mounted at each stable to capture the whole enclosure. The recording period spanned 10–14 nights for each zoo. Thus, 198 nights, with a total of 2772 h, were recorded and analyzed. Observations were conducted by using an all-occurrence sampling for the five behavior states described previously (Martin & Bateson, 2007 ). Video data were analyzed with the software BORIS 2.1.5 (Friard & Gamba, 2016 ). To assess influencing factors on behavior, zoo curators and keepers completed a comprehensive questionnaire, answering questions about husbandry and management (e.g., demographic data and relationship, enclosure size and design, types of food and feeding routines, temperature regulation, and observed abnormal behavior patterns). In addition, keepers filled out a detailed daily protocol to record the daily routine and special events. Using the provided information, the biological background of an animal was considered, including subspecies, sex, age, motherhood (a nursing cow), and use of contraceptives. Furthermore, the following environmental and social conditions were assessed: group size, the presence of a bull in a herd, the way animals were stalled during the night, material used for sleep sites, material of enclosure walls and enclosure size. During the whole observation time, food and water were available ad libitum, but the exact amount of food consumed was not evaluated. As we wanted to show comprehensive overall activity budgets of captive giraffe during night and twilight, total observation period nightly spanned 14 h, starting from 17:00 to 7:00. This time frame was also chosen to ensure for better comparison with existing behavioral studies on giraffe (Duggan et al., 2016 Sicks, 2012 Tobler & Schwierin, 1996 ). Due to housing conditions and the use of artificial light, the length of the dark phase varied between 9 and 14 h among zoos. To account for this great variance, the detailed analysis of influencing variables based solely on the dark phase of the night. For better distinguishability, the term nocturnal from now on defines the period of darkness while nightly covers the 14 h observation period.

2.4 Preparation and analysis of the nocturnal data set

In this study, effects of potential factors influencing the giraffe's nocturnal activity were estimated with linear mixed models (e.g., Cleasby et al., 2015 Harrison et al., 2018 ). To fit the model, behavioral observation data was aggregated for each individual per night. First, univariate correlations between the dependent variables standing activities and REM sleep and various independent variables in zoo giraffe were analyzed. To avoid collinearity, we excluded the variables “material of sleep sites” and “material of enclosure separation walls” from analysis, due to high correlation with most of the other predictor variables. Afterwards, predictors were fitted into an Individual Characteristics Model (IC model) and an Environmental Conditions Model (EC model). Individual characteristics, such as age, sex, subspecies, motherhood, and contraception were unique for each giraffe and were combined in one model. Environmental and social influences such as enclosure size (m²), presence of a bull, group size and the way the animals were stalled (together or separately) were zoo dependent variables and fitted into a second model. Moreover, data on the type and amount of food was collected, but due to the great diversity of feeding management across zoos, it was not possible to categorize this information reasonably for model analyses. The presented two models were then fitted for both standing activities and REM sleep as responsible variables. In total, four models were used to analyze the effects of the presented explanatory variables on standing activities and REM sleep behavior. Individual identity (ID) and zoo were used as random factors. For better understanding, results will be presented in % per dark phase of the night. The estimate's mathematical unit is presented in % aggregated per individual and night. Null models and reduced models were respectively fitted for standing activities and REM sleep behavior. Afterwards, likelihood ratio tests using the analysis of variance (ANOVA) function were done to identify whether the two random effects ID and zoo were significant. Finally, all predictors were combined in one model, and automatic backward elimination of nonsignificant random and fixed effects was used to determine the most parsimonious prediction models using all available explanatory variables. All analyses were conducted in R (version 3.6.0) using lmer function from the lmer4 package for mixed models (Bates et al., 2015 ). To overcome convergence problems occurring with some of the models, the BOBYQA optimizer (Bound Optimization BY Quadratic Approximation Powell, 2009 ) was used to estimate the model parameters. Otherwise, default settings of the lmer function were applied.


Influence of environmental factors on microorganisms

Changes in environmental conditions affect the life of microorganisms. The physical, chemical, biological factors of the environment can accelerate or inhibit the development of microbes, can change their properties or even cause death.

The environmental factors that have the most noticeable effect on microorganisms include humidity, temperature, acidity and chemical composition of the medium, the effect of light and other physical factors.

Humidity

Microorganisms can live and develop only in an environment with a certain moisture content. Water is necessary for all metabolic processes of microorganisms, for normal osmotic pressure in the microbial cell, to maintain its viability. In different microorganisms, the need for water is not the same. Bacteria are mainly hygrophilous, with a moisture content of less than 20%, their growth stops. For molds, the lower limit of the moisture content of the medium is 15%, and with considerable air humidity and below. The precipitation of water vapor from the air to the surface of the product promotes the multiplication of microorganisms.

When the water content in the medium decreases, the growth of microorganisms slows down and may completely stop. Therefore, dry foods can be stored considerably longer than products with high humidity. Drying the products allows the products to be stored at room temperature without cooling.

Some microbes are very resistant to drying, some bacteria and yeast in the dried state can persist for up to a month or more. Spores of bacteria and mold fungi remain viable in the absence of moisture tens, and sometimes hundreds of years.

Temperature

Temperature is the most important factor for the development of microorganisms. For each of the microorganisms, there is a minimum, optimum and maximum temperature regime for growth. By this property, microbes are divided into three groups:

  • psychrophiles are microorganisms that grow well at low temperatures with a minimum at -10-0 ° C, an optimum at 10-15 ° C
  • mesophils are microorganisms for which the growth optimum is observed at 25-35 ° C, a minimum at 5-10 ° C, a maximum at 50-60 ° C
  • thermophiles are microorganisms that grow well at relatively high temperatures with an optimum growth at 50-65 ° C, a maximum at temperatures above 70 ° C.

Most microorganisms belong to mesophiles, for the development of which the temperature is 25-35 ° C. Therefore, the storage of food products at this temperature leads to a rapid multiplication in them of microorganisms and spoilage of products. Some microbes, with significant accumulation in products, can lead to human food poisoning. Pathogenic microorganisms, i.e. The causes of human infectious diseases also belong to mesophiles.

Low temperatures slow down the growth of microorganisms, but do not kill them. In chilled foods, the growth of microorganisms is slow, but continues. At temperatures below 0 ° C, most microbes stop multiplying when the products are frozen, the growth of microbes stops, some of them gradually die off. It was found that at a temperature below 0 ° C most microorganisms fall into a state similar to anabiosis, retain their viability and with the rise in temperature continue their development. This property of microorganisms should be taken into account when storing and further cooking food. For example, salmonella can be stored in frozen meat for a long time, and after defrosting meat, they quickly accumulate in favorable conditions to a dangerous amount for humans.

When exposed to a high temperature, exceeding the maximum of endurance of microorganisms, their dying occurs. Bacteria that do not have the ability to form spores die by heating in a humid environment to 60-70 ° C in 15-30 minutes, up to 80-100 ° C – after a few seconds or minutes. The bacterial spores have a much higher temperature resistance. They are able to withstand 100 ° C for 1-6 hours, at a temperature of 120-130 ° C bacteria spores in a humid environment die in 20-30 minutes. Spores of molds are less heat resistant.

Thermal culinary processing of food in public catering, pasteurization and sterilization of food products in the food industry lead to partial or complete (sterilization) of the death of vegetative cells of microorganisms.

When pasteurized, the food product undergoes minimal temperature effects. Depending on the temperature regime, low and high pasteurization is distinguished.

Low pasteurization is carried out at a temperature not exceeding 65-80 ° C, at least 20 minutes for greater safety of the product.

High pasteurization is a short-term (no more than 1 min) effect on the pasteurized product of a temperature above 90 ° C, which leads to the death of the pathogenic non-spore-forming microflora and at the same time does not entail any significant changes in the natural properties of the pasteurized products. Pasteurized products can not be stored without cold.

Sterilization provides for the release of the product from all forms of microorganisms, including spores. Sterilization of canned canned food is carried out in special devices – autoclaves (under steam pressure) at a temperature of 110-125 ° C for 20-60 minutes. Sterilization provides the possibility of long-term storage of canned food. Milk is sterilized by ultra high temperature treatment (at temperatures above 130 ° C) for a few seconds, which allows you to preserve all the beneficial properties of milk.

The reaction of the medium

The life activity of microorganisms depends on the concentration of hydrogen (H + ) or hydroxyl (OH – ) ions in the substrate on which they develop. For most bacteria, neutral (pH about 7) or slightly alkaline medium is most favorable. Moldy mushrooms and yeast grow well with a weakly acid reaction of the medium. High acidity of the medium (pH below 4.0) prevents the development of bacteria, but molds can continue to grow in a more acidic environment. Suppressing the growth of putrefactive microorganisms during acidification of the environment has practical application. The addition of acetic acid is used in the marinating of products, which inhibits rotting processes and allows the preservation of products. The lactic acid formed during quenching also suppresses the growth of putrefactive bacteria.

Concentration of salt and sugar

Cookery salt and sugar have long been used to increase the resistance of products to microbial damage and better preservation of food.

An increase in the content of dissolved substances (salt or sugar) in the nutrient medium affects the amount of osmotic pressure inside the microorganisms, causes their dehydration. With an increase in the concentration of table salt in the substrate of more than 3-4% multiplication of many, including putrefactive, microorganisms slows down, at a concentration of more than 7-12% – ceases.

Some microorganisms need for their development in high concentrations of salt (20% and higher). They are called salt-loving, or halophiles. They can cause damage to salty foods.

High concentrations of sugar (above 55-65%) stop the reproduction of most microorganisms, this is used when preparing jam, jam or jam from fruit and berries. However, these products can also be damaged as a result of reproduction of osmophilic molds or yeast.

Shine

Some microorganisms need light for normal development, but for most of them it is disastrous. Ultraviolet rays of the sun have bactericidal action, ie, at certain radiation doses lead to the death of microorganisms. The bactericidal properties of the ultraviolet rays of mercury-quartz lamps are used to disinfect air, water, and certain food products. Infrared rays can also cause death of microbes due to thermal effects. The impact of these rays is used in the heat treatment of products. Negative effects on microorganisms can have electromagnetic fields, ionizing radiation and other physical factors of the environment.

Chemical factors

Some chemicals can have a harmful effect on microorganisms. Chemicals that have a bactericidal effect are called antiseptics. These include disinfectants (bleach, hypochlorites, etc.) used in medicine, food industry and public catering.

Some antiseptics are used as food additives (sorbic and benzoic acids, etc.) in the production of juices, caviar, creams, salads and other products.

Biological factors

Different relationships can be established between different microorganisms: symbiosis is a mutually beneficial relationship metabiosis – the vital activity of one at the expense of the other without causing harm parasitism – the vital activity of one at the expense of another with causing harm to him antagonism – one of the types of microorganisms depresses the development of another, which can lead to the death of microbes. For example, the development of lactic acid bacteria inhibits the growth of putrefactive, these antagonistic relationships are used in the souring of vegetables or to maintain normal microflora in the human intestine.

The antagonistic properties of some microorganisms are explained by their ability to release into the environment substances that have antimicrobial (bacteriostatic, bactericidal or fungicidal) action, antibiotics. Antibiotics are produced mainly by fungi, less often by bacteria, they exert their specific effect on certain types of bacteria or fungi (fungicidal action). Antibiotics are used in medicine (penicillin, levomycetin, streptomycin, etc.), in livestock as a feed additive, in the food industry for preserving food (nisin).

Phytoncides – substances found in many plants and food products (onion, garlic, radish, horseradish, spices, etc.) have antibiotic properties. Phytoncides include essential oils, anthocyanins and other substances. They are capable of causing the death of pathogenic microorganisms and putrefactive bacteria.

In egg white, fish eggs, tears, saliva contains lysozyme – an antibiotic substance of animal origin.


Watch the video: Lecture 10: Microbial Biotechnology (December 2021).