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Teaching about evolution

So, we’re supposed to teach our students about evolution – but where to start? What topics to cover? And in what order should we cover them? And for each topic, what are the relevant learning standards? This sequence works for me:

Chicken or the egg

Abiogenesis & spontaneous generation

Abiogenesis – modern discoveries

Charles Darwin’s Voyage of Discovery and Darwin’s notebook

Darwin’s finches

Fossils: Evidence of evolution over time and Dating of fossils

Convergent evolution and Homologous and analogous structures

Natural selection

Artificial selection

clades & phylogenies

clades rotate = equivalent phylogenies

Gradualism vs. Punctuated Equilibrium

Examples of evolution

Evolution of our kidneys

Evolution of humans

Evolution of whales

Where did the idea of evolution develop? How has the idea of evolution changed over time?

Advanced topics

Evolution of the first animals

Ontogeny and Phylogeny: Addressing misconceptions

Did nerves evolve twice?

Horizontal Gene Transfer and Kleptoplasty

Evolution and the 2nd law of thermodynamics

Scars of evolution

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Teaching about cells

So, we’re supposed to teach our students biology – but where to start? What topics to cover? And in what order should we cover them? And for each topic, what are the relevant learning standards? This sequence works for me:

Characteristics of Life
Organelles, an introduction
Organelles: In more depth

What is the role of enzymes in cells?

Diffusion

Osmosis

Endocytosis and exocytosis

Then we move on to types of cells

Bacteria

Archaea

Now the bitty-gritty: Cell reproduction

Mitosis

Asexual reproduction

Meiosis

For those teaching Honors Biology

Active transport across cell membranes

Ion channels and carrier proteins

Endosymbiosis: origin of eukaryotic cells from prokaryotes

Psychopathy

Psychopathy, sometimes considered synonymous with sociopathy, is a personality disorder characterized by persistent antisocial behavior, impaired empathy and remorse, and bold, disinhibited, egotistical traits.

The Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD) introduced the diagnoses of antisocial personality disorder (ASPD) and dissocial personality disorder respectively, stating that these diagnoses have been referred to as psychopathy or sociopathy.  (Antisocial personality disorder#Psychopathy)

Psychopathy has been proposed as a specifier under an alternative model for ASPD. In the DSM-5, under “Alternative DSM-5 Model for Personality Disorders”, ASPD with psychopathic features is described as characterized by “a lack of anxiety or fear and by a bold interpersonal style that may mask maladaptive behaviors (e.g., fraudulence).” Low levels of withdrawal and high levels of attention-seeking combined with low anxiety are associated with “social potency” and “stress immunity” in psychopathy.

Theodore Millon suggested 5 subtypes of ASPD.

Subtype Features
Nomadic antisocial (including schizoid and avoidant features) Drifters; roamers, vagrants; adventurer, itinerant vagabonds, tramps, wanderers; they typically easy to adapt in difficult situations, shrewd and impulsive. Mood centers in doom and invincibility.
Malevolent antisocial (including sadistic and paranoid features) Belligerent, mordant, rancorous, vicious, sadistic, malignant, brutal, resentful; anticipates betrayal and punishment; desires revenge; truculent, callous, fearless; guiltless; many dangerous criminal fits this criteria.
Covetous antisocial (including negativistic features) Rapacious, begrudging, discontentedly yearning; an angle was seen as assertively hostile as to dominate; was envious, seek more profit, and avariciously greedy; pleasures more in taking than in having.
Risk-taking antisocial(including histrionic features) Dauntless, venturesome, intrepid, bold, audacious, daring; reckless, foolhardy, heedless; unfazed by hazard; pursues perilous ventures.
Reputation-defending antisocial (including narcissisticfeatures) Needs to be thought of as infallible, unbreakable, indomitable, formidable, inviolable; intransigent when status is questioned; overreactive to slights.

The study of psychopathy is an active field of research.

Unfortunately the term is used by the general public, popular press, and in fictional portrayals in a variety of contradictory and non-scientific ways, and occasionally as an ad homenim remark.

Conduct disorder

A prolonged pattern of antisocial behavior in childhood and/or adolescence, and may be seen as a precursor to Antisocial personality disorder (ASPD), also known as sociopathy. The DSM allows differentiating between childhood onset before age 10, and adolescent onset at age 10 and later. Childhood onset is argued to be more due to a personality disorder caused by neurological deficits interacting with an adverse environment. 

The DSM-5 includes a specifier for those with conduct disorder who also display a callous, unemotional interpersonal style across multiple settings and relationships. The specifier is based on research which suggests that those with conduct disorder who also meet criteria for the specifier tend to have a more severe form of the disorder with an earlier onset as well as a different response to treatment. – Wikipedia

Is Conduct disorder compulsory in Psychopathy?

Cherie Valeithian, I am a licensed psychologist

In a word, yes, at least when using The Diagnostic and Statistical Manual of Mental and Emotional Disorders, published by the American Psychiatric Association, and currently in it’s 5th edition. The official name for psychopathy/sociopathy is Antisocial Personality Disorder, which is diagnosed only in individuals age 18 or older. One of the criteria required for that diagnosis is that the person met criteria for Conduct Disorder prior to the age of 18, whether or not the person was ever officially diagnosed as such….

https://www.quora.com/Is-Conduct-disorder-compulsory-in-Psychopathy

Hare Psychopathy Checklist

The Hare PCL-R contains two parts, a semi-structured interview and a review of the subject’s file records and history. During the evaluation, the clinician scores 20 items that measure central elements of the psychopathic character. The items cover the nature of the subject’s interpersonal relationships; his or her affective or emotional involvement; responses to other people and to situations; evidence of social deviance; and lifestyle. The material thus covers two key aspects that help define the psychopath: selfish and unfeeling victimization of other people, and an unstable and antisocial lifestyle.

The twenty traits assessed by the PCL-R score are:

  • glib and superficial charm
  • grandiose (exaggeratedly high) estimation of self
  • need for stimulation
  • pathological lying
  • cunning and manipulativeness
  • lack of remorse or guilt
  • shallow affect (superficial emotional responsiveness)
  • callousness and lack of empathy
  • parasitic lifestyle
  • poor behavioral controls
  • sexual promiscuity
  • early behavior problems
  • lack of realistic long-term goals
  • impulsivity
  • irresponsibility
  • failure to accept responsibility for own actions
  • many short-term marital relationships
  • juvenile delinquency
  • revocation of conditional release
  • criminal versatility

The interview portion of the evaluation covers the subject’s background, including such items as work and educational history; marital and family status; and criminal background. Because psychopaths lie frequently and easily, the information they provide must be confirmed by a review of the documents in the subject’s case history.

Results

When properly completed by a qualified professional, the PCL-R provides a total score that indicates how closely the test subject matches the “perfect” score that a classic or prototypical psychopath would rate. Each of the twenty items is given a score of 0, 1, or 2 based on how well it applies to the subject being tested.

A prototypical psychopath would receive a maximum score of 40.
One with absolutely no psychopathic traits would receive a score of zero.
A score of 30 or above qualifies a person for a diagnosis of psychopathy.
People with no criminal backgrounds normally score around 5.
Many non-psychopathic criminal offenders score around 22.

Encyclopedia of Mental Disorders: http://www.minddisorders.com/Flu-Inv/Hare-Psychopathy-Checklist.html

Psychiatry

Psychiatry is a medical  field devoted to the diagnosis, study, and treatment of mental disorders. The following intro has been excerpted/adapted from Wikipedia:

ai-brain

Psychiatric assessment of a person typically begins with a case history and mental status examination. Physical examinations and psychological tests may be conducted. On occasion, neuroimaging or other neurophysiological techniques are used.

Mental disorders are often diagnosed in accordance with criteria listed in diagnostic manuals such as the widely used Diagnostic and Statistical Manual of Mental Disorders (DSM), published by the American Psychiatric Association (APA), and the International Classification of Diseases (ICD), edited and used by the World Health Organization (WHO).

Psychopharmacology became an integral part of psychiatry starting with Otto Loewi‘s discovery of the neuromodulatory properties of acetylcholine; thus identifying it as the first-known neurotransmitter. Neuroimaging was first utilized as a tool for psychiatry in the 1980s.

subpage_people_brain_scans_03

The discovery of chlorpromazine‘s effectiveness in treating schizophrenia in 1952 revolutionized treatment of the disorder, as did lithium carbonate‘s ability to stabilize mood highs and lows in bipolar disorder in 1948.

Biopsychiatric research has shown reproducible abnormalities of brain structure and function, and a strong genetic component for a number of psychiatric disorders. It has elucidated some of the mechanisms of action of medications that are effective in treating some of these disorders.

Still, this research has not progressed to the stage that they can identify clear biomarkers of these disorders.

Research has shown that serious neurobiological disorders such as schizophrenia reveal reproducible abnormalities of brain structure (such as ventricular enlargement) and function. Compelling evidence exists that disorders including schizophrenia, bipolar disorder, and autism to name a few have a strong genetic component. Still, brain science has not advanced to the point where scientists or clinicians can point to readily discernible pathologic lesions or genetic abnormalities that in and of themselves serve as reliable or predictive biomarkers of a given mental disorder or mental disorders as a group.

Ultimately, no gross anatomical lesion such as a tumor may ever be found; rather, mental disorders will likely be proven to represent disorders of intercellular communication; or of disrupted neural circuitry. Research already has elucidated some of the mechanisms of action of medications that are effective for depression, schizophrenia, anxiety, attention deficit, and cognitive disorders such as Alzheimer’s disease. These medications clearly exert influence on specific neurotransmitters, naturally occurring brain chemicals that effect, or regulate, communication between neurons in regions of the brain that control mood, complex reasoning, anxiety, and cognition. In 1970, The Nobel Prize was awarded to Julius Axelrod, Ph.D., of the National Institute of Mental Health, for his discovery of how anti-depressant medications regulate the availability of neurotransmitters such as norepinephrine in the synapses, or gaps, between nerve cells.

— American Psychiatric Association, Statement on Diagnosis and Treatment of Mental Disorders[2]

Related articles

Is a ‘Spectrum’ the Best Way to Talk About Autism?

Learning styles and multiple intelligences

Psychopathy

 

When can correlation equal causation?

Lesson excerpted from The Logic of Science blog

“Correlation does not equal causation.” … although useful, the phrase can be misleading because it often leads to the misconception that correlation can never equal causation, when in reality there are situations in which you can use correlation to infer causation.

Why correlation doesn’t always equal causation

When X and Y are correlated, why can’t we automatically assume that the change in X is causing the change in Y?

There are four possible explanations for why X and Y would change together:

  1. X is causing Y to change
  2. Y is causing X to change
  3. A third variable (Z) is causing both of them to change
  4. The relationship isn’t real and is being caused by chance

[So we] can’t jump to the conclusion that X is causing Y. Further, in most cases, these four possibilities can’t be disentangled. For more details see Why correlation doesn’t have to mean causation

One of my personal favorites is the correlation between ice cream sales and drowning. As ice cream sales increase, so do drowning accidents. Does that mean that eating ice cream is causing people to drown? Of course not.  [Clearly] a third variable (time of the year/temperature) is driving both the drowning accidents and the ice cream sales (i.e., people both swim more often and eat more ice cream when it is hot, resulting in a correlation between drowning and eating ice cream that is not at all causal).

Additionally, sometimes two things really do correlate tightly just by chance. The website tylervigen.com has collected a bunch of these, such as the comical correlation between the number of films that Nicholas Cage stars in and the number of drowning accidents in a given year (everything correlates with drowning for some reason)….

 Correlation can equal causation

All scientific tests rely on correlation – there is a way to go from correlation to causation: controlled experiments.

If, for example, a scientist does a large, double-blind, randomized controlled trial of a new drug (X) and finds that people who take it have increased levels of Y, we could then say that taking X is correlated with increased levels of Y, but we could also say that taking X causes increased levels of Y.

The key difference is that in this case, we controlled all of the other possibilities such that only X and Y changed. In other words, we eliminated the possibilities other than causation.

[Consider the misleading] correlation between autism rates and organic food sales, but this time let’s say that someone was actually testing the notion that organic food causes autism (obviously it doesn’t, but just go with it for the example).

Therefore, they select a large group of young children of similar age, sex, ethnicity, medication use, etc. They randomly assign half of them to a treatment group that will eat only organic food, and they randomly assign the other half to a control group that will eat only non-organic food.

Further, they blind the study so that none of the doctors, parents, or children know what group they are in. Then, they record whether or not the children develop autism.

Now, for the sake of example, let’s say that at the end, they find that the children who ate only organic food have significantly higher autism rates than those who ate non-organic food. As with the drug example earlier, it would be accurate to say that autism and organic food are correlated, but it would also be fair to say that organic food causes autism (again, it doesn’t, it’s just an example).

So, how is this different than the previous example where we simply showed that, over time, organic food sales and autism rates are correlated? Quite simply, the key difference is that this time, we controlled the confounding factors so that the only differences between the groups were the food (X). Therefore, we have good reason to think that the food (X) was actually causing the autism (Y), because nothing else changed.

Let’s walk through this step by step, starting with the general correlation between organic food sales (X) and autism rates (Y) and looking at each of the four possibilities I talked about earlier.

  1. Could organic food be causing autism? Yes
  2. Could autism be causing people to buy more organic food? Yes (perhaps families with an autistic family member become more concerned about health and, therefore, buy organic food [note: organic food isn’t actually healthier])
  3. Could a third variable be causing both of them? Maybe, though I have difficulty coming up with a plausible mechanism in this particular case.
  4. Could the relationship be from chance? Absolutely. Indeed, this is the most likely answer.

Now, let’s do the same thing, but with the controlled experiment.

  1. Could the organic diet be causing autism? Yes
  2. Could autism be causing the diet? No, because diet was the experimental variable (i.e., the thing we were manipulating), thus changes in it preceded changes in the response variable (autism).
  3. Could it be caused by a third variable? No, because we randomized and controlled for confounding variables. This is critically important. To assign causation, you must ensure that the X and Y variables are the only things that are changing/differ among your groups.
  4. Could the relationship be from chance? Technically yes, but statistically unlikely.

Is the difference clear now? In the controlled experiment, we could assign causation because changes in X preceded changes in Y (thus Y couldn’t be causing X) and nothing other than X and Y changed. Therefore, X was most likely causing the changes in Y.

That “most likely” clause is an important one that I want to spend a few moments on. Science does not deal in proof, nor does it provide conclusions that we are 100% certain of. Rather, it tells us what is most likely true given the current evidence… The fact that science does not give us absolute certainty does not mean that it is unreliable. Science clearly works, and the ability to assign probabilities is a vast improvement over the utter guesswork that we have without it.

 Assigning specific causation when general causation has already been established

Next, I want to talk about causes where you can use a correlation between X and Y as evidence of causation based on an existing knowledge of causal relationships between X and Y.

In other words, if it is already known that X causes Y, then you can look at specific instances where X and Y are increasing together (if it is a positive relationship) and say, “X is causing at least part of that change in Y” (or, more accurately, “probably causing”).

Smoking and lung bronchial cancer rates correlation

Smoking and lung/bronchial cancer rates (data via the CDC). P < 0.0001

Let me use an example that I have used before to illustrate this. Look at the data to the right on smoking rates and lung cancer in the US. There is a clear correlation (lung cancer decreases as smoking rates decrease), and I don’t think that anyone would take issue with me saying that the decrease in smoking was probably at least partially the cause for the decrease in lung cancer rates.

Now, why can I make that claim? After all, if we run this through our previous four possibilities, surely we can come up with other explanations.

So, why can I say, with a high degree of confidence, that the smoking rate is probably contributing to the decrease? Quite simply, because a causal relationship between smoking and lung cancer has already been established.

In other words, we already know from previous studies that smoking (X) causes lung cancer (Y). Therefore, we already know that an increase in smoking will cause an increase in lung cancer and a decrease in smoking will cause a decrease in lung cancer.

Therefore, when we look at situations like this, we can conclude that the decrease in smoking is contributing to the decrease in cancer rates because causation has already been established.

To be clear, other factors might be at play as well, and, ideally, we would measure those and determine how much each one is contributing, but even with those other factors, our prior knowledge tells us that smoking should be a causal factor.

This same line of reasoning is what lets us look at things like the correlation between climate change and CO2 and conclude that the CO2 is causing the change. We already know from other studies that CO2 traps heat and drives the earth’s climate. Indeed, we already know that increases in CO2 cause the climate to warm. Therefore, just like in our smoking example, we can conclude that CO2 is a causal factor in the current warming.

Further, in this case, we have also measured all of the other potential contributors and determined that CO2 is the primary one (I explained the evidence in detail with citations to the relevant studies herehere, and here, so please read those before arguing with me in the comments).

The same thing applies to the correlation between vaccines and the decline in childhood diseases. Multiple studies have already established a causal relationship (i.e., vaccines reduce diseases), therefore we know that vaccines were a major contributor to the reduction in childhood diseases (more details and sources here).

Argument from ignorance fallacies

Finally, I want to talk about a common, and invalid, argument that people often use when presenting a correlation as evidence of causation (here I am talking about examples like in the first section where the results aren’t from controlled studies and causation has not previously been established).

I often find that people defend their assertions of causation with arguments like, “well what else could it be?” or “prove that it was something else.” For example, one who is claiming that vaccines cause autism  might defend their argument by insisting that unless a skeptic can prove that something else is causing the supposed increase in autism rates, then it is valid to conclude that vaccines are the cause.

There are two closely related logical problems occurring here. The first is known as shifting the burden of proof. The person who is making a claim is always responsible for providing evidence to back up their claim, and shifting the burden happens when, rather than providing evidence in support of their position, the person making the claim simply insists that their opponent has to disprove the claim.

That’s not how logic works. You have to back up your own position, and your opponent is not obligated to refute your position until you have provided actual evidence in support of it.

The second problem is the argument from ignorance fallacy. This happens when you use a gap in our knowledge as evidence of the thing that you are arguing for.

A good example of this would be someone who says, “well you can’t prove that aliens aren’t visiting earth, therefore, they are” or, at the very least, “therefore my belief that they are is justified.”

Do you see how that works? An absence of evidence is just that: a lack of knowledge. You can’t use that lack of knowledge as evidence of something else.

Conclusion

If you can control for all of those other factors and ensure that the changes in X precede the changes in Y and only X and Y are changing, then you can establish causation within the confidence limits of your statistics.

 

Organelles in depth

Cell membrane

has 2 layers of lipids (fats); hence > lipid bilayer.

Often we see simplified 2D drawings, showing a small section of the lipid bilayer.

Here we see the two layers, and some proteins.

 

Cytoplasm

The thick viscous liquid filling the cell. All the organelles float in it; and it’s also filled with millions of enzymes and other chemicals.

Dancing Queen molecules in cytoplasm

Here is a (false color) visualization of proteins in a cell’s cytoplasm – notice how densely packed this is.

Densely packed proteins in cytoplasm

Nucleus

The command-and-control center of the cell.

Here we see a more realistic image of the nucleus (lower left); we see mRNA copies of DNA coming out of the nucleus through nuclear pores.

Nucleus to ribosomes to ER GIF Protein synthesis NPR

Nucleus to ribosomes to ER GIF from NPR: Protein synthesis

Chromosomes

If we magnify a cell, we can see “X” shaped chunks floating in the cell nucleus.
These chunks are called chromosomes. They are made of a chemical called DNA.

organism cell chromosome DNA 2

Here we see a cell nucleus being lysed (broken open) and all the chromosomes are spilling out on the right.

(The color was added by hand to make it easier to tell them apart.)

We can then cut-and-paste each of the chromosomes, number them, and line them up (lower left.)

In humans we find 23 pairs of chromosomes in every cell.

These X shaped chromosomes are not solid; they are like objects made of wound-up yarn.

A chromosome could be unwound into a long, thing string.

This string is made of DNA molecules.

chromosomes 1

Each section of the chromosome has difference sequences of DNA.

A complete sequence of DNA is called a gene; it is an instruction on how to build a protein.

 

Mitochondrion

and

 

The Endomembrane system

More details: The endomembrane system is composed of the different membranes that are suspended in the cytoplasm within a eukaryotic cell.

Endomembrane system by Mariana Ruiz Villarreal LadyofHats

Endomembrane system by Mariana Ruiz Villarreal, LadyofHats

Ribosomes

In this simplified diagram we note that ribosomes are very tiny compared to the size of a cell.

They are just seen as little black dots. They are way tinier than any of the other organelles.

plant and animal cell ribosomes

Here we see a more realistic 3D model of a cell;

On the right we can just barely see the ribosomes as small dots stuck to the ER.

On the left we see the ER magnified, and the ribosomes are a bit clearer. (Although they are still small; we don’t see any details.)

Darryl Leja, NHGRI Rough endoplasmic reticulum and ribosomes

Darryl Leja, NHGRI Rough endoplasmic reticulum and ribosomes

Some ribosomes also float freely in the cytoplasm.

Here we see mRNA copies of DNA coming out of a cell nucleus, and moving to a type of ribosome floating nearby.

Nucleus to ribosomes to ER GIF Protein synthesis NPR

Nucleus to ribosomes to ER GIF from NPR: Protein synthesis

And now you’ll always remember this:

Beyonce look like the rough ER

 

Cytoskeleton

and a photo

 

Golgi body

Golgi GIF

and

Endomembrane system by Mariana Ruiz Villarreal LadyofHats

Endomembrane system by Mariana Ruiz Villarreal, LadyofHats

ER

Rough ER GIF

and

Darryl Leja, NHGRI Rough endoplasmic reticulum and ribosomes

Darryl Leja, NHGRI Rough endoplasmic reticulum and ribosomes

lysosome

Lysosome GIF

vacuoles

Vacuole GIF

chloroplasts

and

Chloroplast structure Thylakoid Granum

cell wall

Animal cell versus plant cell

and

Plant cell has a wall adapaproject

Plant cell has a wall adapaproject

large central vacuole

 

External resources

http://www.amoebasisters.com/gifs.html

Biology MCAS exams

Previous MCAS exams from the Massachusetts Department of Elementary and Secondary Education

massachusetts-dese-learning-standards

Feb 2017 Biology MCAS

Feb 2016 Biology MCAS

Feb 2015 Biology MCAS

Feb 2014 Biology MCAS

Feb 2013 Biology MCAS

Below you will find each released short-response question, open-response question, and writing prompt that was included on High School Biology MCAS tests; the scoring guide for each question; and a sample of student work at each score point for that question. Taken together, these provide a picture of the expectations for student performance on the MCAS tests.

 

 

 

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