My Dominant Hemisphere

Courtesy Lydia Elle @ Flickr (by-nc license)

I just finished a great lecture series on the history of mathematics by Dr. David Bressoud recently¹. Remember how I once spoke about elegance in inelegance? How some people have argued (eg: Lee Smolin) that the universe just might be complex by nature? How mankind might just be wrong about looking for simple and thus elegant solutions to explain physical phenomena?

Well, I was pretty intrigued by some of the stuff I learned about Henri Poincare’s work in this regard. Poincare is famous for a number of things, his Poincare conjecture being the most obvious of them. A Russian math guru, Grigori Perelman, apparently proved this conjecture some years back and among other peculiar things, not only declined the Fields medal but also a million dollar prize for solving one of the toughest math problems ever known.

But I was particularly piqued by how Poincare was fascinated by this idea of finding elegance and hidden patterns even where one might expect junk. Here are what might be interesting questions as crude examples:

Take a random set of 100 beads. Throw these beads on the floor. They scatter randomly. How many throws would be needed to find at least three beads on the floor that yield an equilateral triangle when they are connected? How many throws would you need to find a cluster of beads that is of a certain shape or size?

That there is some sense of order even in randomness and chaos, is truly an enchanting concept.

Have any thoughts of your own? Do send in your feedback !
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1. Queen Of The Sciences (Lectures by David Bressoud)
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Copyright © Firas MR. All rights reserved.

zaveqna@flickr (by-nc-sa license)

I’ve been studying mathematics lately and really enjoying it. Here’s an interesting story about the history of the trigonometric function, ‘sine‘.

Early in the 1st millenium A.D., a new way of thinking about chords was coming about. The chord is defined as the straight line that joins two points on the circumference of a circle. The ancient Greeks had developed trigonometric functions to calculate the length of arbitrary chords. But several centuries later, by the early 1st millenium A.D., mathematicians in India began to think about calculating and working with half-chord lengths instead. For this, they developed the familiar ’sine’ and ‘cosine‘ functions that we still use to this day. The earliest accounts of the use of the half-chord in Indian texts, is from the Surya Siddhanta (c. 300 – 400 AD), written in Sanskrit. The sound of the Sanskrit word used for ‘half-chord’ was ardha-jya [ardha = half, jya = chord]. Perhaps they found this word too long and eventually it was shortened to jya or jiva for all practical purposes.

By roughly the end of the 1st millenium A.D., the vanguard of scientific growth was now in the hands of the Arab world. In translating the works from Sanskrit into Arabic, scholars in the Arab world transliterated and pronounced jiva as jiba [جب]. The sound ‘jiba‘ is recorded in Arabic as two consonants j [ج] and b [ب] with no vowels explicitly written between them. The vowel sounds are merely implied.

Several centuries later, after the decline of scientific growth in the Arab world, came the Europeans. When they in turn came upon the Arabic word for jiva and tried to translate it, they of course ended up with a word, ‘jb‘ [pronounced as 'jay bee']. Apparently, they were oblivious of the implied vowel sounds. Things were dandy for the Arab scientists, but the Europeans couldn’t make any sense of the sound ‘jay bee‘ because such a sound doesn’t exist in any of the words in the Arabic language. They found that the closest sound to ‘jay bee‘, was the sound ‘jaib‘ or ‘ja-eeb‘, in the Arabic word for the mammary gland! And so the Europeans assumed that the half-chord was to be referred to with a Latin word that meant mamma, mammary gland or any of its other synonyms. Perhaps out of modesty, it was ultimately instead decided that the word used for the fold of a cloth utilized to cover a mamma would be appropriate to refer to a half-chord. This word was ’sinus’. And from this Latin word ‘sinus‘, ultimately came the English word ‘sine‘ that is in use today!

Remarkable, isn’t it?

Feel free to send in your feedback, corrections and comments .

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References:

1. Queen Of The Sciences (Lectures by David Bressoud)

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Copyright © Firas MR. All rights reserved.

A reviewer at the National Institutes of Health evaluates a grant proposal. (Wikipedia)

I managed to read quite a number of interesting books in the last couple of months. Among them, was Scientific Writing: Easy When You Know How by Jennifer Peat et al. Marvelous book and one that I highly recommend. The book has been mainly written for health professionals. It gives you an insider’s view of how the entire peer(expert)-review process in scientific publishing works. There are also interesting nuggets on peer-review outside of medical journals such as conferences, scientific meetings, etc.

The publishing process in a nutshell:

1. Upon submission to a journal, a paper will first go through preliminary screening by special staff who check for typographical errors. Not scientific merit. Did you stick to the word limit? Are the margins, fonts and spaces in accordance with the journal’s ‘instructions to authors‘ policy? If not, the paper will bounce back like rejected email!
2. If it does scrape through, it goes to an editorial committee. Editors in turn run an ambiguous check on the paper’s scientific rigor and impact, whether it appeals to their sensibilities and whether it makes business sense to get it out in their journal. It is then forwarded to external reviewers.
3. Many journals maintain databases of potential external reviewers who are ‘experts’ in their fields, some of whom are on contract for the journal and others who are not. These reviewers have a track record of being active in other journals and meetings. Journals may even rank reviewers based on whether they review papers on time, their general demeanor with authors of papers, etc. Often these chaps are perched in just about every nook and corner of the world. They look at the paper’s strengths and weaknesses in terms of study design, whether the conclusions put forth are in accordance with the reported results, whether the statistics measure up, whether certain areas need clarification, whether some parts should be rephrased or even omitted altogether. Their comments and annotations are then forwarded to the editors and in turn to the authors.
4. Both editors and reviewers often refer to checklists to standardize this process, even if it be somewhat ambiguous. Because different people have different mental cutoffs for ‘clinical significance’ when it comes to reported results, different people will reach different conclusions even if they look at the same ’statistically significant’ data. When two reviewers differ in what they think about a paper, editors will often request a third reviewer to look at it.
5. After a lot of back and forth communication between authors, editors and reviewers the paper is finally published. The editorial committee is the final arbiter that decides whether or not the paper gets published.
6. This process usually take months, unless there is a good reason.

Here are some interesting facts that you might not know about scientific journals:

1. Multiple surveys have shown that journals are more likely to publish ’statistically significant’ findings. This is an important thing to realize. For any scientific study with a Type 1 error rate of 5%, if the null hypothesis was true you would get a statistically significant result 5% of the time. Purely as a result of random chance. But it’s the 5% of studies that report such a ’statistically significant’ result that are more likely to get published than the remaining 95% of studies that don’t.
2. Most of the scientific literature is biased in favor of content produced in English. Translated works are an extreme minority.
3. The most popular articles in a journal are reviews, editorials, letters, etc. and not research papers. Consequently, journals contain more narrative reviews than genuine research. It’s what keeps them in business.
4. Being published is not necessarily something that is a natural consequence of your scientific caliber or contribution to mankind. It is a very political and arbitrary thing. Maybe the editors or reviewers for the journal are biased against your work. Or it could be that the editors do not think publishing your paper will increase their business, for obscure reasons. Maybe your paper is just too specialized and caters to a minority niche of readers. Editors usually want stuff that sells and increases readership (who by the way, more often care about narrative reviews as mentioned previously), impact factors and profits. Quite similar to newspapers actually. Editors may even decide to publish a paper regardless of what the reviewers think, as long as it makes sense to them to do so!
5. When you submit a paper to a journal for consideration, you immediately transfer whole and sole copyrights to it. You are not permitted to share that paper outside of the research team without prior permission from the editors. Transfer of copyright to journals is pretty common and there are only a minority of fledgling journals out there that give you the luxury of retaining copyrights.
6. Many journals have pre-publication ‘embargoes’. If you have discussed your paper in a scientific conference, meeting, on a random website, with the press … and so on, different journals will have different policies on whether or not such a paper constitutes ‘duplicate’ material. That depends on how many beans you spilled out during such conferences, talks, … etc. and under what circumstances. Did you discuss just the abstract, some random figures and tables or the whole thing? Did you submit the paper before or after such disclosure? Does it constitute a copyright violation? If it’s considered duplicate, it will not be published unless there is a good reason.
7. Transfer of copyright also means that you cannot submit your paper elsewhere or hand out copies of it to colleagues in meetings, conferences, etc. You can’t show off the paper on a website either. As long as the paper is under consideration for publication, you need prior permission from the journal. If the paper is rejected or withdrawn from submission, the copyrights are transferred back to the authors.
8. Different journals will have different time limits on copyright. Some will allow you to maintain a copy on a website or a repository after a number of years have passed. These can rightly be called post-publication ‘embargoes’².
9. Scientific knowledge is thus ultimately controlled by vested interests making it difficult for a free and open society. This has led to calls for reform in peer-reviewed scientific publishing, including the open-access movement. There are two main models in open-access: Open-access journals, that make all peer-reviewed content free to the public. Journals from the Public Library Of Science (PLoS) are a good example. Open-access self-archives are another model. Authors can deposit copies (a.k.a. ’self-archives’) of pre-prints or post-prints of articles that they have submitted to non-open-access, peer-reviewed journals that agree to such activity. They can then share these self-archives using websites and other tools. However, often self-archives are deposited in repositories which are usually institutional. Such repositories allow free public access not only to peer-reviewed scholarly content, but also non-peer-reviewed content such as theses and other gray literature. OAIster is a good example of a cross-repository search engine¹.
10. In certain cases you may want to submit your research for urgent publishing. Different journals will call these kinds of papers by different names – ‘rapid response’, ‘rapid paper‘ …, etc. Often they do not contain too much detail as to study design or statistical rigor. These papers will be submitted by editors to external reviewers on the condition that they be reviewed within a specified time frame. Once such a paper has been accepted and published, you may not be able to submit an addendum or supplement later as it might be considered ‘duplicate’ material!
11. Following reporting guidelines such as those mentioned at the Equator Network, will improve your chances of being published.
12. Submitting your paper to a specialty journal increases your chances of success. Most papers fulfill a niche and so do most specialty journals.
13. The chances of you being struck by lightning are higher than the chances that your paper will be accepted without modification. Nearly always, editors and reviewers will get back asking you to change your paper in some way.
14. In highly specialized fields, many journals will use the same set of reviewers. If you disagree with a reviewer and choose to withdraw your submission, it will not do you much good to submit to a different journal.
15. Reviewers are usually free to remain anonymous to authors. And some journals will let authors be anonymous to reviewers in the interest of fairness. However, anonymity does not always happen.
16. If you are well known in your field, don’t be surprised if you receive an offer to expert-review a paper from a random journal.
17. Despite how enticing it sounds, reviewers do not make a lot of money from this business!
18. Different journals select editors using different criteria. At the end of the day, it is the business team of a journal that usually decides. A candidate who can improve a journal’s appeal, impact factor and business profits ultimately wins.

Have anything else to share that’s not on the list? Send me your feedback and I’ll put it up here!

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Your feedback counts:

1. Special thanks to Stevan Harnad of Open Access Archivangelism fame for corrections in the comments. Matt Warren writes in to talk about the NIH’s involvement in open-access. Their Pubmed Central service is worth checking out. ^{[go back]}
2. With regards to ‘embargoes’ and copyrights, Christina Pikas writes in to say that most of this stuff is part of the ‘copyright transfer agreement’, which should always be examined carefully. She also says that many institutions can influence how many rights you have and that if your work was done for a corporation, a corporate lawyer will often help you in the process. Just to add a tiny point, the book that I referred to above mentions that many institutions have policies on copyright and intellectual property (IP) for their departments. Some will allow researchers to hold on to IP rights, while others will take over these IP rights from them. It’s always a good idea to check with your institution or department. ^{[go back]}

Copyright © Firas MR. All rights reserved.

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This is an example of a full genome sequencing machine. It is the ABI PRISM 3100 Genetic Analyzer. Sequencers like it completely automate the process of sequencing the entire genome. Yes, even yours! [Courtesy: Wikipedia]

Some Background Before The Tour

Ahoy readers! I’ve had the opportunity to read a number of books recently. Among them, is “Developing Bioinformatics Computer Skills” by Cynthia Gibas and Per Jambeck. I dived into the book straight away, having no basic knowledge at all of what comprises the field of bioinformatics. Actually, it was quite like the first time I started medical college. On our first day, we were handed a tiny handbook on human anatomy, called “Handbook Of General Anatomy” by B D Chaurasia. Until actually opening that book, absolutely no one in the class had any idea of what Medicine truly was. All we had with us were impressions of charismatic white-coats who could, as if by magic, diagnose all kinds of weird things by the mere touch of a hand. Not to mention, legendary tales from the likes of Discovery Channel. Oh yes, our expectations were of epic proportions . As we flipped through the pages of that little book, we were flabbergasted by the sheer volume of information that one had to rote. It had soon become clear to us, what medicine was all about – Physiology is the study of normal body functions akin to physics, Anatomy is the study of the structural organization of the human body a la geography … – and this set us on the path to learning to endure an avalanche of learn-by-rote information for the rest of our lives.

Bioinformatics is shrouded in mystery for most medics. Because, so many of these ideas are completely new. The technologies are new. The data available are new. Before the human genome was sequenced, there was virtually no point of using computers to understand genes and alleles. Most of what needed to be sorted out could be done by hand. But now that we have huge volumes of data, and data that are growing at an exponential rate at that, it makes sense to use computers to connect the dots and frame hypotheses. I guess, bioinformatics is a conundrum to most other people too – whether you are coming from a math background, a computer science background or a biology background – we all have something missing from our repertoire of knowledge and skills.

What is the rationale behind using computation to understand genes? In yore times, all we had were a couple of known genes. We had the tools of Mendelian genetics and linkage analysis to solve most of the genetic mysteries. The human genome project changed that. We are suddenly flooded not only with sequences that we don’t know anything about, but also the gigantic hurdle of finding relationships between them. To give you a sense of the magnitude of numbers we’re talking about here: we could simplify DNA’s 3-D structure and represent the entire genetic code contained in a single polynucleotide strand of the human genome, as a string of letters A, C, G or T each representing a given nucleic acid (base) in a long sequence (like so …..ATCGTTACGTAAAA…..). Since it has been found that this strand is approximately 3 billion bases long, its entire length comes to 3 billion bytes. That’s because each letter A, T, C or G could be thought of as being represented by a single ASCII character. And we all know that an ASCII character is equal to 1 byte of data. Since we are talking about two complementary strands within a molecule of DNA, the amount of information within the genome is 6 billion bytes^§. But human cells are diploid! So the amount of DNA information in the nucleus of a single human cell is 12 billion bytes! That’s 1.2 terabytes of data neatly packed in to the DNA sequence of every cell – we haven’t even begun to talk about the 3-D structure of DNA or the sequence and 3-D structure of RNA and proteins yet!

§ Special thanks to Martijn for bringing this up in the comments: If you really think about it for a moment, bioinformaticians don’t need to store the sequences of both the DNA strands of a genome in a computer, because the sequence of one strand can be derived from the other – they are complementary by definition. If you store 3 billion bytes from one strand, you can easily derive the complementary 3 billion bytes of information on the other strand, provided that the two strands are truly complementary and there aren’t any blips of mismatch mutations between them. Using this concept, you can get away with storing 3 billion bytes and not 6 billion bytes to capture the information in the human genome.

Special thanks also to Dr. Atul Butte ^¥ of Stanford University who dropped by to say that a programmer really doesn’t need a full byte to store a nucleic acid base. A base can be represented by 2 bits (eg. 00 for A, 11 for C, 01 for G and 10 for T). Since 1 byte contains 8 bits, a byte can actually hold 4 bases. Without compression. So 3 billion bases can be held within 750,000,000 bytes. That’s 715 megabytes (1 megabyte = 1048576 bytes), which can easily fit on to an extended-length CD-ROM (not even a DVD). So the entire genetic code from a single polynucleotide strand of the human genome can easily fit on to a single CD-ROM. Since human cells are diploid, with two CD-ROMs – one CD-ROM for each set of chromosomes – you can capture this information for both sets of chromosomes. ^{[go back]}

To compound the issue, we don’t have a taxonomy system in place to describe the sequences we have. When Linnaeus invented his taxonomy system for living things, he used basic morphologic criteria to classify organisms. If it walked like a duck and talked like a duck, it was a duck! But how do you apply this reasoning to genes? You might think, why not classify them by organism? But there’s a more subtle issue here too. Some of these genetic sequences can be classified in to various categories – is this gene a promoter, exon, intron or could it be a sequence that plays a role in growth, death, inflammatory response, and so on. Not only that, many sequences could be found in more than one organism. So how do you solve the problem of classification? Man’s answer to this problem is simple – you don’t!

Here’s how we can get away with that. Simply create a relational database using MySQL, PostgreSQL or what have you and create appropriate links between sequence entries, their functions, etc. Run queries to find relationships and voila, there you have it! This was our first step in developing bioinformatics as a field. Building databases. You can do this with a genetic sequence (a string of letters A for ‘adenine‘, C for ‘cytosine‘, G for ‘guanine‘ and T for ‘thymine‘ …represented like so ATGGCTCCTATGCGGTTAAAATTT….) or with an RNA sequence (a string of letters A for ‘adenine’, C for ‘cytosine, G for ‘guanine’ and U for ‘Uracil‘ like so …AUGGCACCCU…) or even a protein sequence (a string of 20 letters each letter representing one amino acid). By breaking down and simplifying a 3-D structure this way, you can suddenly enhance data storage, retrieval and more importantly, analysis between:

1. Two or more sequences of DNA
2. Two or more sequences of RNA
3. Two or more sequences of Protein

You can even find relationships between:

1. A DNA sequence and an RNA sequence
2. An RNA sequence and a Protein sequence
3. A DNA sequence and a Protein sequence

If you can represent the spatial coordinates of the molecules within a protein 3-D structure as cartesian coordinates (x, y, z), you can even analyze structure not only within a given protein, but also try to predict the best possible 3-D structure for a protein that is hypothetically synthesized by a given DNA or RNA sequence. In fact that is the Holy Grail of bioinformatics today. How to predict protein structure from a DNA sequence? And consequentially, how to manipulate protein structure to suit your needs.

The Tour Begins

Let’s take a tour of what bioinformatics holds for us.

The Ability To Build Relational Databases

We have already discussed this above.

Local Sequence Comparison

An example of sequence alignment. Alignment of 27 avian influenza hemagglutinin protein sequences colored by residue conservation (top) and residue properties (bottom) [Courtesy: Wikipedia]

Before we delve in to the idea of sequence comparisons further, let’s take an example from the bioinformatics book I mentioned to understand how sequence comparisons help in the real world. It speaks of a gene-knockout experiment that targets a specific sequence in the fruit fly’s (Drosophila melanogaster) genome. Knocking this sequence out, results in the flies’ progeny being born without eyes. By knocking this gene – called eyeless – out you learn that it somehow plays an important role in eye development in the fruit fly. There’s a similar (but not quite the same) condition in humans called aniridia, in which eyes develop in the usual manner, except for the lack of an iris. Researchers were able to identify the particular gene that causes aniridia and called it aniridia. By inserting the aniridia gene in to an eyeless-knockout Drosophila’s genome, they observed that suddenly its offspring bore eyes! Remarkable isn’t it? Somehow there’s a connection between two genes separated not only by different species, but also by genera and phyla. To discern how each of these genes functions, you proceed by asking if the two sequences could be the same? How similar would they might be exactly? To answer this question you could do an alignment of the two sequences. This is the absolute basic kind of stuff when we do sequence analysis.

Instead of doing it by hand (which could be possible if the sequences being compared were small), you could find the best alignment between these two long sequences using a program such as BLAST. There are a number of ways BLAST can work. Because the two sequences may have only certain regions that fit nicely, with other regions that don’t – called gaps – you can have multiple ways of aligning them side by side. But what you are interested in, is to find the best fit that maximizes how much they overlap with each other (and minimize gaps). Here’s where computer science comes in to play. In order to maximize overlap, you use the concept of ‘dynamic programming‘. It is helpful to understand dynamic programming as an algorithm rather than a program per se (it’s not like you’ll be sitting in front of a computer and programming code if you want to compare eyeless and aniridia; the BLAST program will do the dirty work for you. It uses dynamic programming code that’s built in to it). Amazingly enough, dynamic programming is not something as hi-fi as you might think. It is apparently the same strategy used in many computer spell-checkers! Little did the bioinformaticians who first developed dynamic programming techniques in genetics know, that the concept of dynamic programming was discovered far earlier than them. There are apparently many such cases in bioinformatics where scientists keep reinventing the wheel, purely because it is such an interdisciplinary field! One of the most common algorithms that is a subset of dynamic programming and that is used for aligning specific sequences within a genome is called the Smith-Waterman algorithm. Like dynamic programming, another useful algorithm in bioinformatics is what is called a greedy algorithm. In a greedy algorithm, you are interested in maximizing overlap in each baby-step as you construct the alignment procedure, without consideration to the final overlap. In other words, it doesn’t matter to you how the sequences overlap in the end as long as each step of the way during the alignment process, you maximize overlap. Other concepts in alignment include, using a (substitution) matrix of possible scores when two letters – each in a sequence – overlap and trying to maximize scores using dynamic programming. Common matrices for this purpose are BLOSUM-62, BLOSUM-45 and PAM (Point Accepted Mutation).

So now that we know the basic idea behind sequence alignment, here’s what you can actually do in sequence analysis:

1. Using alignment, find a sequence from a database (eg. GenBank from the NCBI) that maximizes overlap between it and a sequence that isn’t yet in the database. This way, if you discover some new sequence, you can find relationships between it and known sequences. If the sequence in the database is associated with a given protein, you might be able to look for it in your specimen. This is called pairwise alignment.
2. Just as you can compare two sequences and find out if there is a statistically significant association between them or not, you can also compare multiple sequences at once. This is called multiple sequence alignment.
3. If certain regions of two sequences are the same, it can be inferred that they are conserved across species or organisms despite environmental stresses and evolution. A sequence encoding development of the eye is very likely to remain unchanged across multiple species for which sight is an essential function to survive. Here comes another interesting concept – phylogenetic relationships between organisms at a genetic level. Using alignment it is possible to develop phylogenetic trees and phylogenetic networks that link two or more gene sequences and as a consequence find related proteins.
4. Similar to finding evolutionary homology between sequences as above, one could also look for homology between protein structures – motifs – and then conclude that the regions of DNA encoding these proteins have a certain degree of homology.
5. There are tools in sequence analysis that look at features characteristic of known functioning regions of DNA and see if the same features exist in a random sequence. This process is called gene finding. You’re trying to discover functionality in hitherto unknown sequences of DNA. This is important, as the vast majority of genetic code is as far as we know, non-functional random junk. Could there be some region in this vast ocean of randomness that might, just might have an interesting function? Gene finding uses software that looks for tRNA encoding regions, promoter sites, open reading frames, exon-intron splicing regions, … – in short, the whole gamut of what we know is characteristic of functional code – in random junk. Once a statistically significant result is obtained, you’re ready to test this in a lab!
6. A special situation in sequence alignment is whole genome alignment (or global alignment). That is, finding the best fit between entire genomes of different organisms! Despite how arduous this sounds, the underlying ideas are pretty similar to local sequence alignment. One of the most common dynamic programming algorithms used in whole genome alignment is the Needleman–Wunsch algorithm.

Many of the things discussed for sequence analysis of DNA, have equal counterparts for RNA and proteins.

Protein Structure Property Analysis

Say that you have an amino acid sequence for a protein. There’s nothing in the databases that has your sequence. In order to build a 3-D model of this  protein, you’ll need to predict what could be the best possible shape given the constraints of bond angles, electrostatic forces between constituent atoms, etc. There’s a specific technique that warrants mentioning here – the Ramachandran Plot – that takes information on steric hindrance and plots the probabilities for different 3-D structures of an amino acid sequence. With a 3-D model, you could try to predict this protein’s chemical properties (such as pKa, etc.). You could also look for active sites on this protein that are the crucial regions that bind to substrates, based on known structures of active sites from other proteins… and so on.

This figure depicts an unrooted phylogenetic tree for myosin, a superfamily of proteins. [Courtesy: Wikipedia]

Protein Structure Alignment

This is when you try to find the best fit between two protein structures. The idea is very similar to sequence alignment, only this time the algorithms are a bit different. In most cases, the algorithms for this process are computationally intensive and rely on trial and error. You could build phylogenetic trees based on structural evolutionary homology too.

Protein Fingerprint Analysis

This is basically using computational tools to identify relationships between two or more proteins by analyzing their break-down products – their peptide fingerprints. Using protein fragments, it is possible to compare entire cocktails of different proteins. How does the protein mixture from a human retinal cell, compare to a protein mixture from the retinal cell of a mouse? This kind of stuff, is called Proteomics, because you’re comparing the entire protein from an organism to another. You could also analyze protein fragments from different cells within the same organism to see how they might have evolved or developed.

DNA Micro-array Analysis

A DNA microarray is a slide with hundreds of tiny dots on it. Each dot is tagged with a fluorescent marker that glows under UV (or another form of) light, if the cells within that dot produce a given protein. When a given protein is made, it means that a given genetic sequence is being expressed (or transcribed into RNA which in turn is being translated in to protein). By inoculating these dots with the same population of cells and by measuring the amount of light coming from these dots, you could develop a gene expression profile for these cells. You could then study the expression profiles of these cells under different environmental conditions to see how they behave and change.

You could also inoculate different dots with different cell populations and study how their expression profiles differ. Example: normal gastric epithelium vs cancerous gastric epithelium.

Of course you could try looking at all these light emitting dots with your eyes and count manually. If you want to take a shot at it, you might even be able to tell the difference between the different levels of brightness between dots! But why not use computers to do the job for you? There are software tools out there that can quantitatively measure these expression profiles for you.

Primer Design

There are many experiments and indeed diagnostic tests that use an artificially synthesized DNA sequence to serve as an anchor that flanks a specific region of interest in the DNA of a cell, and amplify this region. By amplify – we mean, make multiple copies. These flanking sequences are also called primers. Applications for example include, amplifying DNA material of the HIV virus to better detect presence or absence of HIV in the blood of a patient. The specific name for this kind of test or experiment is called the polymerase chain reaction. There are a number of other applications of primers such as gene cloning, genetic hybridization, etc. Primers ought to be constructed in specific ways that prevent them from forming loops or binding to non-specific sites on cell DNA. How do you find the best candidate for a primer? Of course, computation!

Metabolomics

A fancy word for modeling metabolic pathways and their relationships using computational analyses. How does the glycolytic pathway relate to some random metabolic pathway found in the neurons of the brain? Computational tools help identify potential relationships between all of these different pathways and help you map them. In fact, there are metabolic pathway maps out there on the web that continually get updated to reflect this fascinating area of ongoing research.

I guess that covers a whole lot of what bioinformatics is all about. When it comes to definitions, some people say that bioinformatics is the application part whereas computational biology is the part that mainly deals with the development of algorithms.

Neologisms Galore!

As you can see, some fancy new words have come into existence as a result of all this frenzied activity:

• Genomics: Strictly speaking, the study of entire genomes of organisms/cells. In bioinformatics, this term is applied to any studies on DNA.
• Transcriptomics: Strictly speaking, the study of entire transcriptomes (the RNA complement of DNA) of organisms/cells. In bioinformatics, this term is applied to any studies on RNA.
• Proteomics: Strictly speaking, the study of entire proteins made by organisms/cells. In bioinformatics, this term is applied to any studies on proteins. Structural biology is a special branch of proteomics that explores the 3-D structure of proteins.
• Metabolomics: The study of entire metabolic pathways in organisms/cells. In bioinformatics, this term is applied to any studies on metabolic pathways and their inter-relationships.

Real World Impact

So what can all of this theoretical ‘data-dredging’ give us anyway? Short answer – hypotheses. Once you have a theoretical hypothesis for something you can test it in the lab. Without forming intelligent hypotheses, humanity might very well take centuries to experiment with every possible permutation or combination of data that has been amassed so far and mind you, which continues to grow as we speak!

Thanks to bioinformatics, we are now discovering genetic relationships between different diseases that were hitherto considered completely unrelated – such as diabetes mellitus and rheumatoid arthritis! Scientists like Dr. Atul Butte ^{[go back]} and his team are trying to reclassify all known diseases using all of the data that we’ve been able to gather from Genomics. Soon, the days of the traditional International Classification of Diseases (ICD) might be gone. We might some day have a genetic ICD!

Sequencing of individual human genomes (technology for this already exists and many commercial entities out there will happily sequence your genome for a fee) could help in detecting or predicting disease susceptibility.

Proteins could be substituted between organisms (a la pig and human insulin) and better yet, completely manipulated to suit an objective – such as drug delivery or effectiveness. Knowing a DNA sequence, would give you enough information to predict protein structure and function, giving you yet another tool in diagnosis.

And the list of possibilities is endless!

Bioinformatics, is thus man’s attempt to making biology and medicine a predictive science .

Further Reading

I haven’t had the chance to read any other books on bioinformatics, what with exams just a couple of months away. Having read, “Developing Bioinformatics Computer Skills“, and found it a little too dense especially in the last couple of chapters, I would only recommend it as an introductory text to someone who already has some knowledge of computer algorithms. Because different algorithms have different caveats and statistical gotchas, it makes sense to have a sound understanding of what each of these algorithms do. Although the authors have done a pretty decent job in describing the essentials, the explanations of the algorithms and how they really function are a bit complicated for the average biologist. It’s difficult for me to recommend a book that I might not have read, but here are two I’m considering worth exploring in the future:

Understanding Bioinformatics by Marketa Zvelebil and Jeremy Baum

Introduction to Bioinformatics: A Theoretical And Practical Approach by Stephen Krawetz and David Womble

As books to refresh my knowledge of molecular biology and genetics I’m considering the following:

Molecular Biology Of The Cell by Bruce Alberts et al

Molecular Biology Of The Gene by none other than James D Watson himself et al (Of ‘Watson & Crick‘ model of DNA fame)

Let me know if you have any other suggested readings in the comments¹.

There are also a number of excellent Opencourseware lectures on bioinformatics out on the web (example: at AcademicEarth.org. For beginners though, I suggest Dr. Daniel Lopresti’s (Lehigh University) fantastic high level introduction to the field here. Also don’t forget to check out “A Short Course On Synthetic Genomics” by George Church and Craig Venter on Edge.org for a fascinating overview of what might lie ahead in the future! In the race to sequence the human genome, Craig Venter headed the main private company that posed competition to the NIH’s project. His group of researchers ultimately developed a much faster way to sequence the genome than had previously been imagined - the shotgun sequencing method.

Hope you’ve enjoyed this high level tour. Do send in your thoughts, suggestions and corrections!

Copyright © Firas MR. All rights reserved.

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Your feedback counts:

1. Dr. Atul Butte ^¥ suggests checking out some of the excellent material at NCBI’s Bookshelf. ^{[go back]}

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The Monty Hall Paradox

One of the 3 doors hides a car. The other two hide a goat each. In search of a new car, the player picks a door, say 1. The game host then opens one of the other doors, say 3, to reveal a goat and offers to let the player pick door 2 instead of door 1. Is there an advantage if the the player decides to switch? (Courtesy: Wikipedia)

Hola amigos! Yes, I’m back! It’s been eons and I’m sure many of you may have been wondering why I was MIA. Let’s just say it was academia as usual.

This post is unique as it’s probably the first where I’ve actually learned something from contributors and feedback. A very critical audience and pure awesome discussion. The main thrust was going to be an analysis of the question, “If you had to pick an answer in an MCQ randomly, does changing your answer alter the probabilities to success?” and it was my hope to use decision trees to attack the question. I first learned about decision trees and decision analysis in Dr. Harvey Motulsky’s great book, “Intuitive Biostatistics“. I do highly recommend his book. As I pondered over the question, I drew a decision tree that I extrapolated from his book. Thanks to initial feedback from BrownSandokan (my venerable computer scientist friend from yore ) and Dr. Motulsky himself, who was so kind as to write back to just a random reader, it turned out that my diagram was wrong and so was the original analysis. The problem with the original tree (that I’m going to maintain for other readers to see and reflect on here) was that the tree in the book is specifically for a math (or rather logic) problem called the Monty Hall Paradox. You can read more about it here. As you can see, the Monty Hall Paradox is a special kind of unequal conditional probability problem, in which knowing something for sure, influences the probabilities of your guesstimates. It’s a very interesting problem, and has bewildered thousands of people, me included. When it was originally circulated in a popular magazine,  “nearly 1000 PhDs” (cf. Wikipedia) wrote back to say that the solution put forth was wrong, prompting numerous psychoanalytical studies to understand human behavior. A decision tree for such a problem is conceptually different from a decision tree for our question and so my original analysis was incorrect.

So what the heck are decision trees anyway? They are basically conceptual tools that help you make the right decisions given a couple of known probabilities. You draw a line to represent a decision, and explicitly label it with a corresponding probability. To find the final probability for a number of decisions (or lines) in sequence, you multiply or add their individual probabilities. It takes skill and a critical mind to build a correct tree, as I learned. But once you have a tree in front of you, its easier to see the whole picture.

Let’s just ignore decision trees completely for the moment and think in the usual sense. How good an idea is it to change an answer on an MCQ exam such as the USMLE? The Kaplan lecture notes will tell you that your chances of being correct are better off if you don’t. Let’s analyze this. If every question has 1 correct option and 4 incorrect options (the total number of options being 5), then any single try on a random choice gives you a probability of 20% for the correct choice and 80% for the incorrect choice. The odds are higher that on any given attempt, you’ll get the answer wrong. If your choice was correct the first time, it still doesn’t change these basic odds. You are still likely to pick the incorrect choice 80% of the time. Borrowing from the concept of “regression towards the mean” (repeated measurements of something, yield values closer to said thing’s mean), we can apply the same reasoning to this problem. Since the outcomes in question are categorical (binomial to be exact), the measure of central tendency used is the Mode (defined as the most commonly or frequently occurring thing in a series). In a categorical series – cat, dog, dog, dog, cat – the mode is ‘dog’. Since the Mode in this case happens to be the category “incorrect”, if you pick a random answer and repeat this multiple times, you are more likely to pick an incorrect answer! See, it all make sense ! It’s not voodoo after all !

Coming back to decision analysis, just as there’s a way to prove the solution to the Monty Hall Paradox using decision trees, there’s also a way to prove our point on the MCQ problem using decision trees. While I study to polish my understanding of decision trees, building them for either of these problems will be a work in progress. And when I’ve figured it all out, I’ll put them up here. A decision tree for the Monty Hall Paradox can be accessed here.

To end this post, I’m going to complicate our main question a little bit and leave it out in the void. What if on your initial attempt you have no idea which of the answers is correct or incorrect but on your second attempt, your mind suddenly focuses on a structure flaw in one or more of the options? Assuming that an option with a structure flaw can’t be correct, wouldn’t this be akin to Monty showing the goat? One possible structure flaw, could be an option that doesn’t make grammatical sense when combined with the stem of the question. Does that mean you should switch? Leave your comments below!

Hope you’ve found this post interesting. Adios for now!

Copyright © Firas MR. All rights reserved.

Readability grades for this post:

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Readings:

Intuitive Biostatistics, by Harvey Motulsky

The Monty Hall Problem: The Remarkable Story Of Math’s Most Contentious Brain Teaser, by Jason Rosenhouse

usmle, decisionanalysis, decisiontree, examination, examinationtechnique, “examination technique”, “decision analysis”, “decision tree’, montyhallproblem, “monty hall problem”

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Source, Author and License

I was watching a documentary on the Nat Geo channel the other day. It was about medicine of course. A comment made by one of the ‘scientists’ from the show tickled me. He said something to the effect of ‘the more we learn about the human body, the dumber we feel’ . There isn’t a molecule in my body that doesn’t agree with that (pun intended)! LOL! . My deliberations on the nature of the medical profession lie here.

Let’s just think for a moment about the very definition of being dumb:

• Jovial question. Is it your IQ score ?

IQ = Mental Age/Chronological Age x 100

• When Einstein was confronted by the stalwarts of Quantum Mechanics and the probablistic world that their theories entailed, he suggested that probability comes into the picture when you don’t know the absolute truth about something. “God doesn’t throw dice” he said. That means the more proabablistic your lingo, the lesser your knowledge about the actual truth and the dumber you are! LOL!
• On the other side of the divide, proponents of Quantum Mechanics believed that it’s better to use probability than to assume this or that about the universe. In their thinking, this approach was the smarter one.

The last two arguments are pretty much applicable to the tussle in the medical world too, so far as I can tell. There’s a lot of parallelism between Einstein’s views and the scientist’s comment from the documentary.

What is the absolute truth? Perhaps our world isn’t very definite and structured some scientists like Lee Smolin have argued. Maybe a more ‘inelegant’ (as some would like to put it. ..wait a minute… elegance in inelegance? …darn ‘em philosophers !) or probablistic picture is the absolute truth after all. The philosophical debate continues and the globe watches and waits to no foreseeable end.

Bottomline, our medical world is still very probablistic. We might be on our way to a more structured understanding of the human body, but we aren’t there yet. Whether or not this makes us dumb, I leave this for you to decide!

Do care to leave behind your comments .

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Hello everyone!

Today, I want to talk about an ostensibly innocuous yet profound statement that struck me the other day, from one of my favorite TV shows, NUMB3Rs. In one particular episode, physics professor, Larry Fleinhardt, in his inimitable style said:

“Real science is discovery, Charles, it’s not invention. The truths are there, whether we find them or not.”

What fascinates me most, is how true the first part of of what he said is, at least at a macro level. Because discovery and invention are so intricately meshed together, it is perfectly understandable when this becomes a subject of controversy among scientists and thinkers. A lot of discovery comes about due to invention, and likewise, invention cannot be borne without discovery. On the surface, this might look like the chicken-or-egg problem, but notice that while invention always needs discovery, the reverse is not necessarily true. You cannot truly do something unless you are first conscious about its underlying nature. In essence, the very marrow of science’s purpose, is in the discovery and understanding of truths in our universe. This is by far more valuable and enchanting than anything else. Larry, perhaps, tried to reflect this view on the business of math, but it applies to all of science really. We don’t often think about it very much, but every scientist fits either two roles – theoretical/pure or applied. There might be overlaps in his or her work but the brunt of it falls into one of these domains. While the theorist works around discoveries most of the time, the applied scientist is mainly concerned with inventing stuff based on knowledge gained by discovery, and bringing it to use for humanity. This is also true for medical science. The clinician is the applied scientist whereas the basic scientist is in large part, the purist. In this grand scheme of things, stop for once and think, which one are you?

On a parting note, I leave you with you with snapshots from the captivating lives of Carl Friedrich Gauss, widely referred to as the “greatest mathematician since antiquity”, and Ibn Sina (otherwise known as Avicenna) and his genius, courtesy of Wikipedia. Recall Gauss’ work’s relevance to medics when they deal with the familiar Gaussian curves and Ibn Sina, whom the likes of William Osler considered to be their patriarch.

Feel free to leave behind your comments. Asta la vista for now!

(Mind Blooming with Thoughts – Source)

SETTING THE SCENE

Awrighty folks, before you start pointing fingers at what might seemingly sound like an antithetical piece of work on a blog such as this, yes, I do agree that it does very much sound like philosophical BS . But I believe that a topic such as this deserves an honest look, all the more so amongst the medical fraternity. After all, we all do pledge allegiance to what we think is a ’scientific’ trade, don’t we? Our very existence as professionals depends on it. Therefore a temporary breach of policy has to be made.

What has ignited this train of thoughts you might ask. Well, the truth is that I’ve been grappling with this question ever since I joined medical school and started facing some of the Convoluted Reasoning and Anti-intellectual Pomposity (CRAP for short as beautifully put by this book: Biostatistics – The Bare Essentials) of profs and texts in my academic affairs. It is especially irksome to note that most of this CRAP is presented to you in a package that’s labeled ’scientific’. In this respect, many members of our fraternity lack the candor to call a spade a spade. It is only after I read a chapter in the Oxford Textbook of Medicine many months ago that I’ve come to understand the import of the title of this post. Finally, here was a book that had the courage to face up to it. Having finished all of my undergraduate examinations, the picture’s gotten a lot clearer – all those countless inconsistencies, contradictions and hypocrisies (many as wild as to defy logic & a number of which I’ll touch upon in my future posts) in our textbooks & bed-side classes desperately call for an honest appraisal of how doctors THINK (note the emphasis) and practice. The chapter I’m speaking about is called “Scientific Method & the Art of Healing” (section 2.2 in the ebook format). A remarkable read indeed, one that in my opinion should be required reading for all medics – young & old alike. I strongly urge you folks to have a look at it. In fact all undergraduate curricula should make it a drill to run students by that heckuva chapter the moment they embark on the medical course!

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To give you a sense of what the undercurrents are, the preceding chapter, “Science in Medicine: When, How & What”, begins by saying:

• “At least since the Hippocratics, medicine has always aspired to be scientific. What has changed is not so much the aspirations but what it has meant to be ’scientific’ “

In section 2.2 (the chapter that’s the focus of our current discussion) you’ll find the following (listed in no particular order):

• <Philosophers and historians of science and medicine always seem unhappy when it comes to deciding what is meant by 'scientific medicine'; this is dangerous country for the unwary!
• When Henry Dale, the distinguished British physiologist and pharmacologist, arrived at St Bartholomew’s Hospital as a medical student in 1900 he was told by his first clinical teacher, Samuel Gee, that, as medicine was not a science but merely an empirical art, he must forget all the physiology that he had learnt at Cambridge. This advice reflects a deep-rooted tension between the art and science of clinical practice, which STILL permeates the medical profession.
• Even today, with all our knowledge of their chemistry and physiology, we have a very limited understanding of the mechanisms that underlie most of the diseases that we encounter in day-to-day practice. Caring for sick people involves making considered judgements based on limited evidence and information.
• In view of the remarkable progress in the biological sciences over the last few hundred years, today’s doctors must try to establish the extent to which the balance of medical practice has shifted from ‘craft’ to ’science’.
• How far do the contents of a modern textbook of medicine reflect genuine scientific knowledge as compared with received wisdom and experience?
• At best, we are slowly reaching the stage at which we are aware of how little we know.

In summary, the chapter highlights the fact that medical science as we know it today is really not the straightforward science you’d expect it to be. There are a lot of unknowns & this compels us to turn to more empirical (translated = unscientific) ways of dealing with day-to-day problems. A lot of our textbooks contain pure BS.

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Read Dr. Harrison’s bio at Wikipedia

HOW I THINK OF SCIENCE AND WHERE EXACTLY DOES MEDICINE FIT IN

This brings me to what I view as ’science’. This is important because what I write will not make much sense unless you’re clear about it. Take the preceding paragraph for example. Terms such as ‘medical science’, ’straightforward science’, etc. leave readers in shock & awe at best – if people accept medicine with all its empiricisms as a ’science’ what would you call non-empirical subjects such as physics or mathematics? Superscience ? LOL!!

In my opinion, what distinguishes scientific study from non-science are essentially two interdependent yet separate things :-

• How you THINK
  Thinking scientifically means you rely on carefully acquired principles about subjects & phenomena to logically arrive at conclusions. eg. (i) 2+2 = 4 . (ii) You can’t just get Gold out of Iron because they are two entirely different elements. Unless you manage to change the anatomy of the atoms involved you cannot conceivably bring about that change. Every step of the way you exercise your mind.

• How you ACT
  Acting scientifically involves you applying scientific methods to learn about your surroundings and the universe. Notions such as Observation, Experimentation, etc. all come under this umbrella.

In the end, how you Implement science (i.e build a gadget for example) will depend on both of these things. [Because medicine, as noted below, is deficient in the "THINK" part, I feel it is inexact or incomplete - it is an inexact science (Dr. Atul Gawande for one agrees & although I've not had the opportunity to go through his book, the title, Complications: A Surgeon's Notes on an Imperfect Science, is self-explanatory). Your implementations (treatments for example) are accordingly quite often inexact/empirical.]

Let us mentally shift to that time when we were in high school, shall we? We all studied biology, chemistry, mathematics and physics right? Which one did you consider most involved “principles” and “observation, experimentation, etc”? Agreed, each does touch upon these things; but some do more than the others. To me physics represented & still does to this day that epitome of science. Here’s why:-

• Biology:- Weak in the “How you THINK” part. All you do is memorize this or that part of the anatomy and superficially at best, review the pertinent physiology. This weakness may be due to (i) lack of factual information on the topic or (ii) defects in our approach to thinking about known facts.
• Chemistry:- Essentially very similar to Biology.
• Mathematics:- Strong in ‘THINKING’, Stronger in ‘ACTING’. In the words of statisticians, your numbers are only as good as the information they contain. You feed the wrong information, you get the wrong answers. It’s in choosing what ‘information’ to put into your numbers where I believe mathematics won’t help you to a great degree.
• Physics: Here you have a quest to understand our universe (& by extension all of the things contained therein) at the most fundamental of levels. The emphasis is on both the THINKING and ACTING, equally. What’s more, physics has gradually permeated new domains that were hitherto considered insulated from its reach. Chemistry with its atom is a prime example. And it is likely that this phenomenon will extend to others such as biology, etc. in the future.

There’s thus a definite stratification that’s obvious between them. Medicine, because it’s a branch of biology, shares a lot of its qualities – with weaknesses & gaps in our THINKING. When we think of percussion & auscultation of the Respiratory System for example, our principles (based on known facts in this case, by the way) are rather unrefined :

• Percussion: Gas-resonant vs. Solid-dull.
• Auscultation: Normal gas-filled alveoli –> vesicular (poor transmission of sounds to the chest wall from the larynx, low intensity, low pitch) vs. Solidified tissue/Alveoli filled with fluid –> bronchial (better transmission of the sound, high intensity, high pitch).

We do not delve into questions such as why gas should be resonant as opposed to solid. Are transmission of sound, its intensity & pitch properties different from its resonance? How are they related? Why is it that solid vs. gaseous has seemingly opposite effects on the resonance and intensity/pitch of the sounds that you hear from the respiratory system?

Believe me, docs do end up making mistakes because of this superficial approach. One of our esteemed profs once mistakenly declared that the coin test is positive in open pneumothorax only & negative in closed pneumothorax! He’d apparently gotten confused between the resonance and transmission of sound in the Respiratory System! How he might have later translated that into treatment would therefore have been unscientific. My point here is that a sizable proportion of empiricisms that medicine is fraught with today in reality have a lot to do with flaws in the ways we understand our principles based on known facts (forget the uncertainties due to unknowns). Flaws that can be avoided if we try not to be so superficial and crude in our approach to thought. Unfortunately however, it is a matter of fact that this is not how our fraternity works. We seem quite content in looking the other way . (There are striking differences between the levels of thought of basic scientists such as physiologists and clinicians such as internists. Translational medicine, a recent branch, was born to partly act as a bridge between the two.)

Medicine today, places a lot of emphasis on the ‘How you ACT’ part. The methods behind observation and experimentation have reached reasonable sophistication and continue to develop as we speak (from the dry labs of computational biologists to the clinical research arena, a good deal of progress has & continues to be made). This is what’s made it the science that it is today. Much remains to be done however with the ‘How you THINK’ part – uncovering the unkowns & rectifying our approaches to what we do know. With the advent of mathematical biology, computational biology and a whole set of novel interdisciplinary tools we might look at improvements in this area as well.

SO WHERE DOES ALL THIS LEAVE THE MED STUDENT?

In utter bewilderment! How many med students would agree that their profs realize these truths and test them accordingly? Hardly any. You could get the answers that rely on science right all the time but not those that depend on empiricisms because of the simple fact that the latter vary with time, place and person. Prof Y would say something that went diametric to Prof X’s teachings while the late Prof Z had an entirely different approach! So how does one go about separating empirical BS from hard science in the textbook? Mentally segregate the data handed down to you under the following headings and your job gets a tad easier:

• What/Which
• When
• Where
• How
• Why

If any one of these is shaky, particularly the Why, you might be face to face with an empiricism! This is also true if your data defies logic (On a side note for those of you who think quantum mechanics makes no sense yet the equations work in describing truths, the math there is more or less consistent. Inconsistency is what I’m mainly attacking here.) It is likewise especially important to differentiate weaknesses in our principles due to flaws in our thinking about known data as opposed to lack of factual information (i.e. unknown variables).

Okay, here’s an example of a question that demands an empirical answer that defies logic:

Q. Normal vesicular breath sounds & the Korotkoff sounds are low pitched. You should:

1. use the bell of your stethoscope to hear them
2. use the diaphragm of your stethoscope to hear them

The logical answer here is the bell. But you are sure you saw the profs using the diaphragm!

On a similar note, you might have been taught that the incidence of cardiovascular morbidity/mortality is higher in men than in women but it equalizes as women approach menopause, reflecting the cardioprotective effects of estrogen in women. If this were true how do you explain the paradoxical increased cardiovascular morbidity/mortality in women on estrogen/hormone replacement therapy in Randomized Controlled Trials? Clearly, estrogen’s role as a cardioprotective agent and one that explains differences between male:female incidence should be viewed with skepticism as this defies logic (provided the natural estrogen and the HRT estrogen are similar in composition & their blood levels match) & the first statement in this paragraph should be dismissed.

CONCLUDING THOUGHTS

In spite of all its uncertainties, medicine remains the science of tomorrow. All of its unknowns provide fertile ground for brisk research (so great is its attractiveness that it has drawn some of today’s brightest mathematicians, computational scientists, chemists, engineers, physicists & their like and continues to do so as we speak!). What’s important is for us to endeavor ’scientifically’ in our profession, in the full sense of the term. Acknowledging, in full honesty & magnanimity, that today’s doctor’s central role as a ’scientific’ healer rests as much upon his ability to make decisions based on a conscious lack of what little knowledge of the human body he has as his ability to make them in its presence, is one of the things that lies at the very crux of this idea. What differentiates him and the quack is that he isn’t deluded into thinking that he knows about something when in fact he doesn’t. And if we are to remain the ’scientific’ healers that we initially set out to become, we ought to periodically remind ourselves of this reality.

Among other things, if scientific endeavor demands that we, as doctors, go beyond what conventionally falls under our purview, we must be open to collaborate or make a start in the interdisciplinary sphere. We ought to continually ask ourselves, isn’t that which binds us together as a community our quest to understand the farthest frontiers in biology (= medicine)? As doctors do we not represent the centerpieces in man’s pursuits in this arena?

Even if all of the universe and its parts & phenomena (and by extension medical science) were to be one day modelled & defined by the all-encompassing science of physics, that wouldn’t change the underlying passion that binds us together as a fraternity.

To conclude, are thoughtful words from Richard Feynman, the great Nobel laureate in physics, on the business of scientific endeavor (click here for more on Feynman & Nanomedicine) :-

Read Feynman’s bio at Wikipedia

Why we are here

.. I can live with doubt and uncertainty and not knowing. I think it’s much more interesting to live not knowing than to have answers which might be wrong. I have approximate answers and possible beliefs and different degrees of certainty about different things, but I’m not absolutely sure of anything and there are many things I don’t know anything about, such as whether it means anything to ask why we’re here, and what the question might mean. I might think about it a little bit and if I can’t figure it out, then I go on to something else, but I don’t have to know and answer, I don’t feel frightened by not knowing things, by being lost in a mysterious universe without having any purpose, which is the way it really is so far as I can tell. It doesn’t frighten me.

Source: edited transcript of an interview with Richard Feynman made for the BBC television program Horizon in 1981, in Richard Feynman, the pleasure of finding things out, pg. 25.

In order to progress / Live and not know

The scientist has a lot of experience with ignorance and doubt and uncertainty, and this experience is of great importance, I think. When a scientist doesn’t know the answer to a problem, he is ignorant. When he has a hunch as to what the result is, he is uncertain. And when he is pretty darn sure of what the result is going to be, he is in some doubt. We have found it of paramount importance that in order to progress we must recognize the ignorance and leave room for doubt. Scientific knowledge is a body of statements of varying degrees of certainty – some most unsure, some nearly sure, none absolutely certain.

Now, we scientists are used to this, and we take it for granted that it is perfectly consistent to be unsure – that it is possible to live and not know. But I don’t know everybody realizes that this is true. Our freedom to doubt was born of a struggle against authority in the early days of science. It was a very deep and very strong struggle. Permit us to question – to doubt, that’s all – not to be sure. And I think it is important that we do not forget the importance of this struggle and thus perhaps lose what we have gained.

Source: What Do You Care What Other People Think? Further Adventures of a Curious Character by Richard Feynman as told to Ralph Leighton. In Richard Feynman, the pleasure of finding things out, pg. 146.

Feel free to leave behind your comments! Shalom/Salaam!
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Copyright © Firas MR. All rights reserved.

Ever stop to wonder what “Catastrophe Theory“ is? Park’s T/B of SPM in the chapter, ‘Environment & Health’ mentions how Global Warming and Catastrophe Theory are related. Small changes in the environment, due to their mutually reinforcing nature can lead to cataclysmic events.

Akin to the multistage launching of a space-ship or satellite, the use of the “Jump flap” technique/wrist-transported abdominal “pedicled tube flaps” for reconstructive surgery of the face, lower limb, etc. never ceases to amaze students of surgery. Pedicled-tube flaps were first pioneered by Sir Dr. Harold Gilles who is considered as the father of modern plastic surgery and who widely used tube flaps during the First World War. The Gilles Archive at Queen Mary’s Hospital, UK contains fascinating pictures of his work. For more on the contributions of Dr. Gilles one might also want to check out Project Facade, an interesting venture that aims to compare plastic surgery as it was practised during WWI to how it is practised now, depicting how far the science has come & what the future might hold. War Surgery & Medicine, a book maintained by the New Zealand Electronic Text Center contains some interesting historical photographs depicting the jump-flap technique of reconstructive surgery.