#please i only have the methods + materials section written out and then that graph and those tables

[tiixij]

how am i supposed to keep this lab report to 3 pages maximum when ive got an entire page taken up my a graph and two tables hmm???

[stemosphere-blog]

Stem-O-Sphere’s Tips and Tricks for Lab Reports!

Lab reports. Not the kind that involve Golden retrievers in lab coats. Many consider the writing of lab reports to be the worst parts of their courses due to the sheer amount of information that must be included, the rigid format, the VERY FRUSTRATING GRAPH MAKING, and the analysis of confusing results.

Yet, maybe when your professor uttered the words “lab report”, you felt no sense of dread rising inside you. Or maybe you are so lost that you don’t know how you will survive this class assignment and have started to panic. Or maybe you aren’t losing all your marbles, but you are also not anticipating getting much sleep the night before the due date. Whatever your situation, it is still beneficial for you to read this anyways.

Lab reports follow a basic format. Rest assured, no matter the amount of effort applied at least one point for writing down your name, date, course name and number, and your instructor’s name. 😊 But let’s try to aim higher! So here are some tips and tricks to writing a lab report, from the mind of a fellow surviving science student.

1.       START EARLY!!!!!

This may be the single most important way to get ahead of the game. Lab reports will take longer than you will expect, so start as early as possible. Keep reading to find out what else you can do.

2.       The Order

The widely accepted way to tackle a lab report is: 

Materials and methods --> Results --> Introduction --> Discussion/conclusion. 

In other words, here is the way you would be approaching this: you write what you did, what you got, what was your research question, and how do your results answer your research question. However, there is any other order that works better for you, then go for it! Creativity and originality are also valued in science writing, so experiment with different styles.

3.       Captions

When captioning your figures or graphs, mention where they are on the page. Also, make sure that your captions are distinguishable from your writing.

 4.       Organization

Divide your Materials and Methods section by making each paragraph represent a different experiment or task. It will look like this:

“Collection and examination: To begin my experiment and collect an isolated colony, I chose an environmental location to swab and collect a sample of the bacteria present in this site. The location chosen was…”

PCR amplification: Using the 16S rDNA primers, only the section containing that gene is multiplied. To carry out a PCR, two PCR tubes…”

and so on!

5.       Graphics

Your graphics should be legible when printed, however, they should not take up pages and pages of your report! If your report could have been 12 pages with decently sized images but you made the images big enough that it is now 20 pages, your instructors will most likely not be pleased with you.

6.       Buzz Words

Here are some zinger words to use when you’re writing! My personal favorites include aliquot, predominate, and utilize. 

 7.       Play Tunes

Play some jams! It helps time go by quickly. But make sure to pick something that isn’t too catchy that it distracts from your writing. One time I started typing the words to “Shape of You” by Ed Sheeran without realizing it in my Results section! Three-hour music videos like this save lives!!! 

8.       Color Printing

If you need to find a place on the UI campus that does color printing, here is a link!  

9.       Conciseness is a valuable quality in a paper, so watch your page count!

 Besides from these tips, I give you a final trick: be confident in your writing. Do not falter from stating your interpretations, since the main purpose of writing a report is to show how you carried out the scientific process. Regardless of whether you are supposedly “right” or “wrong” in your results, your grade is determined by how you able to think through what you did and why did things turned out the way they did.

To conclude, I will leave you with the words of Edward O. Wilson:

“...successful research doesn't depend on mathematical skill, or even the deep understanding of theory. It depends to a large degree on choosing an important problem and finding a way to solve it, even if imperfectly at first. Very often ambition and entrepreneurial drive, in combination, beat brilliance.” 

Good luck with your writing! And remember that science is for everyone! Ask us if you have any other questions using the Ask button at the top of our Tumblr page ;D

Follow us on Stem-O-Sphere for content like this and of other types!  And reblog to save someone’s GPA!

-Written by Camille Jaime-

[spacefires]

IB Survival Guide: PART ONE

As someone who’s been through the IB program and finished with horrible mental and physical health, here are some tips and tricks so that you guys don’t end up like me :)

Disclaimer: subject specific tips vary for SL/HL students, go to the bottom of the post to see what HLs and SLs I took

IAS

Please please start your IAs early! Split it out across multiple days! Honestly each section takes around 30 minutes for IAs

GET ALL YOUR THOUGHTS ON THAT PAPER AS SOON AS POSSIBLE FOR ANY IA.

Even if it’s the worst rough copy in the history of the planet, get all your words on there, THEN start editing, formatting, and adding pictures. Trust me, this is much less stressful and your final copy will probably be much cleaner

Science IAs- START YOUR EXPERIMENTS EARLY get as many trials as you can in!

Be sure to talk to your teachers about ideas for your papers beforehand!

Geography/English/French IAs- start early for these too. If you’re like me and you have “oh shit” moments when you suddenly get really good ideas half way through your work and have to restart, starting early for these is a good idea. Especially for geo, writing the IA is relatively easy, spreading it out over 4-5 days works well.

Peer editing is always a good idea, people may catch things you didn’t

EEs

CHOOSE A TOPIC YOU LIKE- Interest plays a big part in how well you do, choosing a topic I was interested in made me not mind spending so much time on my EE

Choosing a topic you don’t like may increase your chance of leaving it to the last minute

START YOUR EEs EARLY and have at least 3 drafts. I split mine up over the course of 4 months, and came out with an A on my Geography EE.

Like the IAs, do one section a day, for example, start with introduction, then methodology, etc. etc.

DO NOT LEAVE YOUR EE TO THE LAST MINUTE PLEASE

I highly suggest doing a geo EE because even if you don’t have results you’re okay, you just have to explain why there was no correlation

Geo EE protips: have lots of pretty handdrawn maps, you can have an ok data analysis and still get a good grade, have good methodology, 

discuss your EE with your supervisor often, make sure you udnerstand what you’re studying

EXAMS

Oh boy this is the scariest part of IB for any student

REVIEW REVIEW REVIEW as much as you can!

Study each subject for at least 5 days!

DO AS MANY PRACTICE EXAMS AS YOU CAN

Seriously, doing practice exams helps you get used to IB’s way of asking you things and you have an idea of what to expect

Do the practice exams guys

Just do them

If your school has mock exams, study really hard for them so you get an idea of how you need to study for the actual examsLook back on mistakes you’ve made on tests/mocks

While studying, focus on your weak points, but don’t ignore your strong points-- forgetting things is easy

SLEEP EARLY DURING EXAM WEEK 

Being refreshed and ready to go is important because youll be able to focus better and your brain will work better-- I did this and it was good

If you’re allowed to take snacks into the exam, take snacks into the exam (but not loud snacks, gummies and stuff)

take water into the exam but don’t drink a lot of water-- pee breaks are a waste of time

if you need to pee during an exam try to hold it in

just try to avoid having to pee

ENGLISH/FRENCH EXAMS:

You can study for your English Lit and French A lang lit exams in like a day if you’re not too keen on getting above a 5. Be familiar with the material

Memorize 5-7 important quotes-- preferable really short ones

Even if it’s not mandatory to memorize quotes, sticking quotes in is an asset

Spend 10-20 minutes planning your essay out, get your ideas down on a paper before writing your essay

Remember: quality over quantity, even if you have lots written down, it’s your ideas that get you most of the marks

Use highlighters, highlight important words, quotes, etc in the passages you’re given

annotate your passages

Have a colour coding system for your passages when you highlight, each colour should be an important point, but have 3-4 main important points, so 3-4 colours (this helps with planning as well)

If your prompt is like, discuss 2 OR MORE something something, discuss only 2, it’s easier, and you waste less time planning/writing, and you can have more ideas

SCIENCE/MATH EXAMS:

Practice problems are good, on top of practice exams

Understand the material!!!!!!!!!!! Memorization is not understanding!!! IB asks a lot of questions that require application

If you suck at calculus, try to understand it better and don’t be like me and assume there isn’t going to be a lot of it.

Seriously study the calculus @all you SL students

Study the calculus

GEO EXAMS

Yeah for this you need to memorize really well, see how well you know the material by going out on walks pointing at things and seeing how you can relate it to what you’ve earned

Study from multiple sources for geo, sometimes there are details that are missed

Memorize lots of case studies and stats!!!!!!!

memorize graphs and maps too, drawing them to support your points in your answers shows how well you understand the material

STUDYING

REVIEW OH MY GOD REVIEW E V E R Y T H I N G as much as possible throughout the year!!!!!!

Tips to force yourself to review:

Take shitty notes in class

This way you have to retake good notes-- wow you’re going over material that was previously taught!

Make cheatsheets! Even if you don’t use them during tests, cheatsheets are a great way to have all your material on one page and ohmygod look at you you’re reviewing your notes again to condense them!

Flash cards work too

Find a way to enjoy writing notes--  for me, I like using fancy pens and highlighters, that way I looked forward to doing it

Find a study environment you like! A cafe, the library, the park, even a different part of the house

Changing your study environment can also help you focus-- a change of scenery helps sometimes and you won’t get bored!

Talk to yourself

Seriously just explain concepts to yourself talk to yourself hearing yourself say it makes the info sink in better

Make really weird mnemonics idk it worked for me

Group studying can help for courses that need discussion in order to better understand concepts-- Geo, English and French

Explaining things to people also helps

Do your homework kids-- even if your teacher doesn’t check it, always find time to do your homework

Do things based on a level of priority

example: I have a test and a big project worth lts of marks due tomorrow (I would focus on the project, but still study for the test enough to have a good grasp of the material)

I know tests don’t count for IB but this is what your teacher bases your predicteds off of, so study hard for them kids. It is also a method of review

Most teachers understand how students can get extremely stressed out with the amount of work we get, if you need an extension for a non-IB related thing, you should be able to ~politely~ ask them

Time management is key, set up schedules for yourself

If you’re studying something you hate, go hard at it for a limited amount of time, then go and study for something you like, or take a brain break

TAKE BREAKS MAN

seriously taking breaks while studying is good

Use apps like forest to keep you focused

reward systems are good too, I do it with chocolate (one piece everytime I finish a chapter)

TOK

 lol good luck

The essay and the presentation are tough-- but you can do it.

The nice thing about TOK is it’s mostly a thought dump, so dump your thoughts before organizing them into an essay

Discuss TOK things with your friends a lot-- you’ll understand better, trust me, you’ll also get good ideas for presentations and stuff!

Get an interesting topic for your TOK presentation ok

discussion is the best advice I can give you guys

TOK is a special course

be prepared to get very angry because all your thoughts will contradict each other

existential crises are fun

That should be about it for Part 1 really, this is mainly academics based, I might add to this as time goes on.

If you have questions!!!! I took Chem HL, Bio HL, Geo HL, English Lit SL, French A Lang Lit SL, and Math SL, and did my EE in Geo. I’m happy to help any young ones out :))

Good Luck all you IB Students! You guys are brave, you can do it :)

[naivelocus]

Evolutionary dynamics of incubation periods

Essential revisions:

1) All the reviewers found the calculations extremely interesting and considered the results to be novel and important. However, there was a shared concern that the connection of these results with the biological phenomenon of the incubation period was not on firm ground. In particular, the assumption that 100% – or close to 100% – of the host cells are infected when symptomatic infection starts was not well-motivated, and the biological plausibility of this assumption was unclear.

We agree, and have addressed this point in a new paragraph added to Results, called “Testing robustness to update rule and truncation.” The relevant sentences are: “For example, suppose we allow symptoms to occur when invaders take over only a fraction f of the whole network. This is a reasonable consideration as leukemic cells need not take over all the bone marrow before leukemia becomes evident, nor does typhoid need to overwhelm all the cells in the microbiome before causing fever; indeed it is likely far fewer in both cases.” Figure 6 (D, E, F) show that the right-skewed distribution persists for Death-birth dynamics, even if only 10% takeover triggers the appearance of symptoms. (Actually, the right-skewed distribution can be proven to persist for any fixed, nonzero fraction f. We cite the relevant math literature in the text.)

On the other hand, Birth-death dynamics are sensitive to the choice of f. They produce a distribution that switches from a Gumbel when f = 100% to a normal distribution for any f less than 100%.

We explain why the two cases differ in the caption to Figure 6. “The difference in sensitivity between the two types of dynamics can be explained intuitively by when coupon collection occurs. […] In contrast, coupon collection occurs near the end of the invasion for Birth-death dynamics (when residents are scarce), and hence gets truncated when f < 1, giving rise to a normal instead of a right-skewed distribution.”

2) We would like to encourage the authors to reconsider the interpretation of their findings. Ideally they should provide a broader a list of biological phenomena (not only incubation periods) that could be described by their model.

We appreciate this helpful suggestion. In response, we have added the following paragraph at the end of the Discussion: “Aside from their possible application to incubation processes, our results also shed light on a broader theoretical question in evolutionary dynamics: when a mutant invades a structured population of residents, how does the distribution of mutant fixation times depend on the network structure of the population? […] We hope that our exact results for disparate topologies and dynamics will stimulate further investigations of these important questions in evolutionary biology.”

3) If they want to strengthen their interpretation of incubation periods, a mechanistic description how the model describes specific diseases is needed along with a discussion of possible caveats.

We have tried to connect the model more closely to specific diseases. In the section of the Results called “Mathematical model,” the relevant passage reads: “A network of N ≫ 1 nodes is used to represent an environment within a host where a pathogenic agent, such as a bacterium or cancer cell, is invading and reproducing. The network could represent several plausible biological scenarios, for example the intestinal microbiome, where harmful typhoid bacteria are competing against a benign resident population of gut flora in a mixing system (modeled as a complete graph); or it could represent mutated leukemic stem-cells vying for space against healthy hematopoietic stem cells within the well-organized three-dimensional bone marrow space (modeled as a 3D lattice); or a flat epithelial sheet with an early squamous cancer compromising and invading nearby healthy cells (modeled as a 2D lattice).”

Regarding possible caveats, we have clarified (in Results, “Mathematical model”) that the model probably does not apply to viruses:

“While Sartwell’s law has been applied to diseases as varied as measles and leukemia, the model we propose makes the most sense for asexually reproducing invaders, like cancer cells or bacteria. […] So, while the general phenomenon of network invasion seems to apply to viruses as well, this model is not well suited to describe their dynamics.”

We also draw a closer connection to cancer dynamics in the Discussion: “On the other hand, it is tempting to speculate that this regime of nearly neutral fitness may be more relevant to cancer development. […] This is also consistent with the suggestion of (Williams and Bjerknes, 1972); the shape of tumors in the model most closely resembled that of real tumors when the fitness of the invaders was only slightly above to neutral.”

4) It would also be of interest to discuss the question of time-varying N (either growing N, or declining N as a result of the pathogen killing host cells). However, following consultation, it was felt that anything more than discussion of this question is beyond the scope of the current work.

We are also interested in the effect of time-varying N, but also agree that a full investigation is outside of the scope of this paper. To give a preliminary sense of what might occur, we have added a new section in the Materials and methods, “Influence of non-static population,” as well as an additional figure (Figure 9). These show the results of an initial exploration of the effects of a non-static population on the complete graph. We specifically examine cases where the resident population has a chance at growing at every time step, shrinking at every time step, and randomly growing or shrinking at each time step.

Reviewer #1:

[…] My primary concerns surround the relatively limited connection back to the biology. The authors consider multiple different possibilities for within-host network connectivity and provide some biological justification for the different topologies (e.g., structured tissue = 3d lattice, epithelium = 2d lattice, etc.); however:

1) The analysis of the Moran model focuses on the time to fixation of the invading allele (i.e. 100% infection of the modeled network); however, immune activation does not wait until 100% of the population is infected but rather activates at a much lower threshold (admittedly again in a stochastic fashion). One of the key findings of this work is that a right-skewed distribution arises due to the "coupon collector problem" where the last few uninfected nodes in the graph can take a long time to be infected, particularly for high-dimensional topologies. However, if the incubation period were to end when, say, any 1% of cells were infected rather than 100%, then the coupon collector problem seems less (or perhaps not at all) relevant.

Please see our response to Essential revisions comment 1.

2) Infection / reproduction in a spatial situation will often happen in parallel rather than serially. The argument that high-dimensional topology may lead to a slow down of the final infections appears predicated on the idea that the rate of infection is constant due to the serial nature of the Moran model – but the overall infection rate presumably increases as a function of the surface area of the infected volume (and analogously for other topologies). Does this really not matter for the distribution of time to fixation? This area is not my specialty, but the theory surrounding Fisher's wave of advance may be relevant.

In the models presented, the overall infection rate does indeed grow with the surface area of the infected population. This has been clarified in Materials and methods, “Birth-death, other solvable networks,” with the following sentences: “For a two-dimensional square lattice, it is more difficult to produce analytical results that are both rigorous and exact. But by making an approximation based on the geometry of the lattice, and using the fact that the population growth rate is proportional to its surface area (see the Appendix, "Normally distributed fixation times for 2D lattice"), we can make a non-rigorous analytical guess about the distribution of the fixation times T.”

This is also elaborated upon in the Appendix, "Normally distributed fixation times for 2D lattice," with: “Assuming this to be true, recall the basic dynamics of infinite-r Bd with N nodes and m invaders. First, we uniformly select one node out of the population of invaders, which is always a probability of 1/m per node. Then we replace one of the invader's neighbors, uniformly at random. However, only invaders on the surface of the cluster even have a chance at replacing a healthy node!

Given sufficient regularity of the boundary of the cluster, this means that the probability of an invader replacing a healthy node is proportional to

qm=1m⋅

3) Many pathogens cause infections in a bursting fashion (e.g., lytic viruses) for which it is not obvious if the Moran model assumption of one random birth / one random death at a time is relevant.

We agree, and we have now clarified (in Results, “Mathematical model”) that the model probably does not apply to viruses: “While Sartwell's law has been applied to diseases as varied as measles and leukemia, the model we propose makes the most sense for asexually reproducing invaders, like cancer cells or bacteria. […] So, while the general phenomenon of network invasion seems to apply to viruses as well, this model is not well suited to describe their dynamics.”

Reviewer #2:

Ottino-Loeffler et al. propose a simple and elegant solution, based on invasion dynamics in structured population, to the interesting observation that incubation periods for a variety of diseases are right-skewed. I found the idea to be very elegant, the text well-written, and overall the arguments to be compelling and easy to follow. I especially liked the interpretation for the dispersal coefficients naturally ranging between certain limits. However, I have a few concerns/questions that I would like to see addressed:

1) In order to apply the ideas of evolutionary graph theory the authors assume that the populations are finite (all graphs have only N nodes); however, this is not always true (certainly not for all the diseases that the authors mention in the abstract and introduction) and I would have liked to have seen from the authors at the very least an acknowledgement of this strong assumption and a discussion of how relaxing this assumption might affect the conclusions. It would be even better (and really interesting) if the authors could pick one example of dynamically-growing network (this should be doable at least for the complete graph) and see how that affects their predictions.

Please see our response to Essential revisions comment 4.

2) The Death-birth (DB) dynamic that the authors employ is actually different from the DB dynamic proposed by Ohtsuki et al. 2006 (unlike the BD dynamic which is the same). Ohtsuki et al. consider death to be random (all nodes have probability 1/N) and birth competition to be among the neighbors, proportional to their fitness. Of course, it's no problem proposing a new variant; I'm just curious whether the authors had a biological reason for choosing this variant of the DB update rule. Especially since they find in their Materials and methods (section on truncation) that the update rule actually matters a lot, a fact that has been observed in evolutionary graph theory more broadly. On that note, I thought this result was sufficiently important that it deserved at least a couple of sentences in the Discussion (rather than just being mentioned in the Materials and methods); I had the same reaction to all the results in that section ("Testing robustness to update rule, fitness, and truncation"), which I thought deserved some mention in the main Discussion.

To clarify this, we have added a feature box (Box 2, “Nomenclature for the Moran Model”), which includes an explanation and table describing and naming the most common Moran models. In particular, we would call the model described by the reviewer “dB,” as opposed to the Db model we used. The relevant sentences are: “To avoid confusion, we use standard abbreviations to distinguish the different models, as illustrated by the table below. […] For example, dB refers to the update rule where the first step uniformly selects a node from the entire population to die, and then one of its neighbors is selected, with probability proportional to fitness, to replace it.”

3) My final point is more a question than a concern: the authors apply this method to in-host dynamics and incubation periods; however, it seems like it could apply to epidemiological questions as well (e.g. spread of flu in a population). Have the authors considered the parallels? Are there any data analogous to incubation periods that could be employed to show the applicability of this model to epidemiological questions as well?

We find this possibility interesting, and have previously considered the parallels. However, a full investigation into the particulars of epidemic dynamics would be beyond the scope of the current paper. Nonetheless a connection can likely be made via evolutionary graph theory, which we mention in a new paragraph added to the Discussion: “Aside from their possible application to incubation processes, our results also shed light on a broader theoretical question in evolutionary dynamics: when a mutant invades a structured population of residents, how does the distribution of mutant fixation times depend on the network structure of the population? […] We hope that our exact results for disparate topologies and dynamics will stimulate further investigations of these important questions in evolutionary biology.”

Reviewer #3:

[...] I am very much in favor of publication of these findings. I do have some questions or concerns regarding the biological interpretation of the paper.

What is the relationship between the model, specifically time to fixation in a Moran process on a graph, and the biological phenomenon of an incubation period. Clarification about the biological motivation for this theoretical framework and for the different update rules could strengthen the paper. In particular, which of the several diseases mentioned in the paper does the model apply to, and why?

Please see our response to Essential revisions comment 3.

Also, clarification regarding the choice of fixation time to quantify the length of the incubation period would strengthen the paper. Lowering the threshold to below 100 percent appears to remove the skew in some instances.

Please see our response to Essential revisions comment 1.

Second, might the distribution of clock times differ from the distribution of step times in the process.

To clarify this important point, we have added a new paragraph to the Results section, “Mathematical model.” The relevant paragraph reads: “Our notion of time in this model is linked directly to the biology of invasion of a reproducing asexual pathogen that divides and replaces other cells sequentially. […] Future iterations of this model could consider deriving an exact scaling between physical time and this biological event-based updating of time.”

Third, certain model choices produce symmetric, rather than skewed, distributions of times to a threshold. I am curious if a more complete investigation of this observation could shed light on the appearance of skewness in different evolutionary scenarios.

This is a fascinating question. A universal condition to ensure the appearance of symmetric distributions is beyond the scope of this paper, but the current conditions appear to involve infinite invader fitness combined with either a low-dimensional network or a truncation condition that avoids the Coupon Collector’s effect.

A final small, technical clarification would help to understand the derivation of skewness when r = 1 at the end of the paper. How is the number of steps which do not change the number of mutants being counted?

For the sake of generality, the specific calculation mentioned here calculates the skew that arises from the random walk alone, not including the waiting times. In the Appendix section “Asymptotic skew of conditioned random walk,” the following sentences have been added to clarify and justify this choice: “We will use the wait-omitted time n in this section as a first-order approximation of the true takeover time. […] Moreover, scaling and numerical arguments based on the results here can show that the bulk of the final distribution is defined by this random-walk process.”

Further, at the end of the fourth paragraph of the subsection “Asymptotic skew of conditioned random walk”, should An(1) be Mn(1)?

Thank you, this has been corrected.

Again I wish to emphasize the great interest and novelty of this work. While some issues associated with the interpretation of the paper remain unclear, I think that the authors will be able to address them by pointing to biological scenarios, perhaps even outside of infection, in which their calculations provide valuable insight.

We have added a paragraph about the model’s wider relevance to evolutionary dynamics: “Aside from their possible application to incubation processes, our results also shed light on a broader theoretical question in evolutionary dynamics: when a mutant invades a structured population of residents, how does the distribution of mutant fixation times depend on the network structure of the population? […] We hope that our exact results for disparate topologies and dynamics will stimulate further investigations of these important questions in evolutionary biology.”

https://doi.org/10.7554/eLife.30212.020

— eLife recent issues