Transapient Musings of an S6 Archailect

What's mine is mine: Brain scans reveal what's behind the aversion to loss of possessions
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Did you ever wonder why it is so difficult to part with your stuff? A new study reveals fascinating insights into the specific neuropsychological mechanisms that are linked with the potential loss of possessions. The research, published by Cell Press in the June 12 issue of the journal Neuron, has important implications for both neuroscience and economics and may even explain why you are reluctant to sell your iPod.

posted at: 12:00 | path: /sci/bio/neuro | permanent link to this entry

The hidden universal distribution of amino acids biosynthetic networks: a genomic perspective on its origins and evolution
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Background:
Twenty amino acids are the universal building blocks of proteins. However, their biosynthetic routes do not appear to be universal from an Escherichia coli-centric perspective. Nevertheless it is necessary to understand their origin and evolution in a global context - i.e. to include more 'model' species and alternative routes-. We use a comparative genomics approach to assess the origin and evolution of amino acid biosynthetic alternative network branches.
Results:
We predicted a core of widely distributed network branches biosynthesizing at least 16 out of the 20 standard amino acids, suggesting that this core occurred in the last common ancestor by tracking the taxonomic distribution of amino acids biosynthetic enzymes. Additionally, we detail the distribution of two types of alternative branches to this core: i) analogs - enzymes that catalyze the same reaction (using the same metabolites) and belong to different superfamilies; and ii) 'alternologs' - herein defined as branches that, proceeding via different metabolites converge to the same end product-. We suggest that the origin of alternative branches is closely related to different environmental metabolite sources and life-styles among species.
Conclusions:
The multi-organismal seed strategy employed in this work improves the precision of dating and evolutionary relationships among amino acids biosynthetic branches. This strategy could be extended to diverse metabolic routes and even other biological processes. Additionally, we introduce the concept of 'alternolog', which not only plays an important role in the relationships between structure and function in biological networks, but also as shown here, has strong implications on their evolution, almost equal to paralogy and analogy.

posted at: 11:54 | path: /sci/bio | permanent link to this entry

"NASA encouraged Europe on Thursday to develop its own manned spaceship, which would give the world -- and particularly the U.S. -- another way of reaching the international space station. Europe became "a full-fledged space power," the agency's administrator said, when flight controllers at a European Space Agency center guided an unmanned cargo ship to the international space station in April, successfully delivering food, water and clothes."

When we see a familiar face, or even a photo of a favorite car or pet, we're often flooded with memories from our past. Sometimes just seeing a person or object that's similar to the ones in our memory will trigger recollections we never knew we had. Maybe you've had a memory triggered by a scent or the texture of an object. Sometimes emotions such as happiness or anger will spur vivid memories, too.

A new study adds an unexpected method to the list of ways to spur memories about our past: body position. That's right: just holding your body in the right position means you'll have faster, more accurate access to certain memories. If you stand as if holding a golf club, you're quicker to remember an event that happened while you were golfing than if you position your body in a non-golfing pose.

Even more fascinating than the facts about body position and memory is how they were learned. A team led by Katinka Dijkstra actually had young adult and older adult volunteers assume different body positions while asking them to remember particular events from their lives. Sometimes the body position matched the memory:

I’m on a team that just got awarded a Keck Futures Initiative grant. This is the fruit of the NAKFI conference we attended last year on “The Future of Human Healthspan.” It was an unusual conference: instead of giving individual talks, the participants were split up into “task groups” that were each assigned a different question related to the biology of aging. At the end of the conference, each group gave a presentation. (The proceedings are available in overpriced booklet form here or as free HTML here).

Our group started off with one subject (stochasticity of gene expression) but took a sharp left turn and ended up thinking more broadly. We ended up focusing on what evolutionary biology might teach us about aging.

Within groups of species that share a given body plan (e.g., bats, birds, dogs, or primates), there is significant variation in maximum life expectancy, and we believe this variation is genetically determined. In other words, natural selection has performed dozens of parallel “experiments” in which more or less similarly constructed organisms end up with different lifespans, based on variations in a range of factors (some known or long-suspected, like antioxidant enzymes, and others as yet undetermined). Some of these factors may be unique to specific body plans, whereas others might be universal. The challenge we set ourselves was ambitious: How can we use the “data set” (i.e., variation in lifespan among related organisms) to identify novel determinants of longevity? Thus was born the Comparative Biogerontology Initiative.

We soon realized that we’d need a great deal of expertise, not only from within biogerontology but also from other fields, some with which we often have dealings (biostatistics, computational biology) and others with which we have almost no interaction in our daily professional lives (veterinary medicine, pathology, histology, comparative physiology). Identifying the relevant experts is a profound challenge in itself: How does one identify expertise in a field in which one has none? Hence a lot of what we’re going to be doing at first is figuring out who our collaborators will be — leading to the contorted mission statement:

These researchers will hold two meetings with senior scholars to develop a plan to test hypotheses about biological factors that control lifespan and healthspan, and compare tissues from multiple species of animals. The scholars are pathologists, comparative physiologists, methodologists, statisticians, and experts in the biology of aging.

“Hold…meetings…to develop a plan to test hypotheses”…no doubt, this will inflame the sensibilities of those who advocate a more direct frontal assault on the problem of aging; indeed, if this were all we were planning to do, they would have a point. We know a lot about aging and it makes sense to move forward aggressively where knowledge is already extensive — but those efforts are being undertaken already, and will continue. All of us are keeping our day jobs.

The CBI was conceived not as a replacement for more direct studies of more relevant models (like humans), but as a complement: by carefully examining aging in understudied organisms, and by systematically identifying the factors that contribute to their differential longevities, our hope is to discover entirely new determinants of aging and lifespan. By bringing in expertise from around the scientific world, including disciplines that don’t usually overlap with biogerontology, our hope is to break new ground in the biology of lifespan (and, if you like, to open new fronts in the battle against aging). In the process, we’ll learn more about the evolutionarily conserved bases of aging throughout the animal kingdom, identify new biomarkers of aging, and pose enough new questions to keep the next generation of biogerontologists busy for years to come.

The other members of the team are, dare I say it, eminences grises of biogerontology — some of whose work and thoughts (e.g., Steve Austad and Richard Miller) we’ve discussed here in the past (and one of whom is my current boss, Judy Campisi). I’m personally thrilled for a chance to work with and learn from them.

And who knows? After we hold our meetings to develop a plan to test a hypothesis, we might actually test one, and then I can blog about it here. Watch this space for further developments.

I am currently reading Daniel Moerman's "Meaning, medicine and the 'placebo effect'". As well as containing many interesting asides, the book discusses what is at the heart of the so-called placebo effect: patients' response to the meaning of their treatment. Moerman calls this the 'meaning response'. This response to meaning explains why two inert pills produce more cures than one inert pill, and why inert injections are even more effective (because "everybody knows" that injections are more powerful than pills). But importantly, it is possible to show that doctors are as important in producing the meaning response as patients. Gracely et al (1985) looked at the effect of placebo on pain in patients having their wisdom teeth extracted. The study was set up as a standard double-blind (neither the doctor nor the patient knows if the patient is getting a real medicine or an inert placebo), with the possibilities being a placebo, fentanyl (which usually reduces pain) and naloxone (which usually blocks reduction in pain, so could be expected to increase the pain of the procedure). The twist was that for the first half of the experiment the doctors, but not the patients, were told that a supply problem meant that no patient would be getting the pain-relieving fentanyl. In the second half the doctors were told that the problem had been resolved, so that now the patients might receive fentanyl. By comparing levels of patient pain in the placebo condition is possible to gauge the effect of doctor expectations on the meaning response of the patients. In this condition patients are all receiving inert substances, and they all 'know' the same thing: they might receive a placebo, pain-relief or 'pain-enhancement'. The doctors don't tell them about the supply problem and, for that matter, they don't know themselves for definite what the patient is given. The only difference is that for the patients in the first half, the doctors think they know that pain-relief is not a possibility, whereas in the second half it is. The graph of the results, copied from Moerman's book is below:

As you can see, patients in the PN group --- those whose doctors thought they might receive pain-relief had a large pain-relieving placebo effect. Those in the PNF group --- those whose doctors thought they couldn't receive pain-relief --- didn't have a pain-relieving placebo effect.

What I think is interesting about this study is, firstly, it confirms the need for rigorous double-blind controls in studies of medicine and, secondly, just how significant an effect this subtle manipulation has. The doctors don't know anything definite, and they certainly aren't telling the patients what they suspect or guess, but somehow --- a look? a slightly brighter smile? a slightly lowered tone? --- they communicate their knowledge of the probabilities to the patients who then experience a real change in their levels of pain because of it.

A striking aspect of the meaning response is that one could suppose that patients have control over their experience of different levels of pain. After all, we know that the pills are inert. Could we just imagine ourselves a 'placebo effect' in all situations where we have unnecessary pain? Sadly, normally we can't do this --- the meaning response doesn't work like that. Doctors are required to give patients permission to feel less pain. Perhaps a fundamental part of the creation of meaning is that it requires other people.

Gracely, R. H., Dubner, R., Deeter, W. R., & Wolskee, P. J. (1985). Clinicians' expectations influence placebo analgesia. Lancet, 1(8419), 43.

Moerman, D. E. (2002). Meaning, medicine, and the "placebo effect". Cambridge University Press: New York.

American company that specialized in locating open funding opportunities across all funding categories in all fifty states.

The same nanotech approaches being explored to deliver drugs exactly to the cells where they are needed also provide a technology base that might lead to permanent enhancements of human metabolism. Excerpts from “Cell ‘organs’ get plastic upgrades“, by Tamsin Osborne at NewScientist.com news service:

Although the immediate interest is in drug delivery, the researchers involved are mindful that more sophisticated artificial organelles could provide metabolic services beyond the natural human repertoire.

I am sure most of you know that Michael Cariaso won the first 23andme Win Your Genome. The reason he did is a clear example of the power of the kinds of tools he used; specifically SNPedia and Promethease. I must admit that bbgm has not given Michael’s efforts quite the attention they deserve. Unlike many others, which might be flashier, they’re rather usable and his understanding of how the web works is no secret.

It also points to the importance of access to underlying data, e.g. 23andme etc allowing customers to export their genotypes (and the panels), without which tools like SNPedia and Promethease would not be too useful. But looking at Prometheus, at Foldit, at folding@home, the molecular workbench or even the rather nice tools provided by 23andme got me thinking. May people get interested in computers at a young age, programming, hacking. Some people become makers in their teens. I wonder, that with open data and scientific apps that are easy to use and accessible by many, are there kids actually playing around with them? When they use folding@home as a screensaver, do people wonder about what’s going on under the hood?

I suppose where I am going with this completely haphazard ramble is that there are tools now which allow us to ask some interesting scientific questions. The other day I talking about Brian Greene’s thoughts on science and the wonders of science. In keeping with that train of though, are today’s kids, or even adults for that matter, learning from the tools mentioned above, playing with them? Is anyone telling them what is happening? I am very very curious.

Update: Changed the title. That’s what happens when you’re half asleep and hit publish

This week in the arxiv: superconductivity update
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Summer writing and travel are eating my blogging time a bit, and I've also agreed to write the occasional nano-related blurb for the ACS. While my posting rate has taken a hit, science has continued to march forward, with a lot of exciting new preprints concerning (relatively) high temperature superconductivity. Here's a sampling....

arxiv:0805.4463 - Matsumoto et al., Superconductivity in undoped T' cuprates with T_c over 30 K
This paper is a perfect example of why materials growers are (unfortunately often unsung) heroes in this field. The authors have come up with a new method for growing cuprate compounds of the form T'Re₂CuO₄, where T'Re is a rare earth from the series (Pr, Nd, Sm, Eu, Gd). Historically these compounds were found to be antiferromagnetic insulators - no superconductivity. In this new work the authors argue that these old results were due to interstitial oxygen leading to pair-breaking. Instead, with the new growth + annealing technique, these compounds are found to exhibit superconductivity with transition temperatures as high as 30 K. These subtleties are why one should always be very careful when looking at suggested compositions in new compounds....

arxiv:0805.4630 - Rotter et al., Superconductivity at 38 K in the iron arsenide (Ba_1-xK_x)Fe₂As₂
This is the first paper I've seen (though I may have missed one) that reports superconductivity in a compound related to the new iron arsenide systems but with two iron arsenide layers per unit cell rather than one. Back in the heyday of the cuprates, the same sort of thing happened - people went from compounds with single copper oxide planes to those with multiple planes per unit cell, and transition temperatures went up. Once again we see how rich the materials landscape can be.

arxiv:0806.0063 - Wang et al., Very high critical field and superior J_c-field performance in NdO_0.82F_0.18FeAs with T_c of 51 K
Other exciting features of the new iron arsenide superconductors are their extremely high critical fields and critical currents. If the transition temperatures could be raised a bit (say past 77 K) and the compounds could be made in wire form (certainly not easy in the cuprates; unlikely to be simple in these either since like the cuprates they are brittle), this could be a huge deal for high field magnets and other applications of superconductivity.

arxiv:0805.4616 - Chen et al., The BCS-like gap in superconductor SmFeAsO_0.85F_0.15
arxiv:0806.0249 - Matano et al., Spin-singlet superconductivity with multiple gaps in PrO_0.89F_0.11FeAs
These two papers examine two related compounds with different techniques, trying to figure out how the charge carriers in these iron arsenides pair up to form the Cooper pairs that make up the superconducting condensate state. In the former, measurements of Andreev reflection (a process where an electron in a normal metal approaches a superconductor, two electrons actually cross into the superconductor, and a hole is "retroreflected" back into the normal metal, leading to a pronounced feature in the conductance of the metal/superconductor interface) strongly suggest that the samarium compound acts like an ordinary BCS superconductor. That is, each Cooper pair has zero angular momentum (s-wave pairing); this implies that the superconducting gap is uniform in momentum space, with no nodes. In contrast, the cuprates exhibit d-wave pairing, with a superconducting gap that has a four-lobe structure in momentum space and that goes to zero along four particular crystallographic directions.

The second paper uses NMR measurements of the Pr compound to argue instead that there are multiple gaps, and further that the pairing symmetry is p-wave (which has been seen in superfluid ³He and in strontium ruthenate). At first glance, these two results seem to disagree, though (a) they are talking about different materials, and (b) the Andreev measurements are particularly sensitive to the surface, while the NMR measurements are nontrivial to interpret, at least for nonexperts. Well, this is the fun part - stay tuned, and we'll see how this shakes out.

posted at: 11:37 | path: /sci/physics | permanent link to this entry

I wish to offer you, my dear reader, the chance to play the part of the experimenter and confront you with a simple measurement setup, wherein a subtlety is hidden -one the experimenter needs to realize if he is to perform his experiment correctly. I will describe the setup and the equipment, and give maybe too much detail, as per my typical style. Then I will formulate a question, giving you one day to ponder on it. I am going to try to make this post so simple that you are not expected to know anything about particle physics in order to participate. All is needed is the knowledge that muons decay, with a lifetime of about two microseconds, producing an energetic electron; but you just acquired that knowledge by reading the last sentence.

The experiment seeks to determine the muon lifetime by stopping muons from cosmic rays inside a thick bar of aluminum, and then measuring the time it takes for an electron to emerge from it. A simplified view of the apparatus is as shown below.

There is a 8-inch shield of lead on top (the blue rectangle), which stops the so-called soft component of cosmic rays (three thin lines ending inside it are shown). This is due to protons, pions, electrons, and photons - all secondaries produced by the high-energy interaction of a cosmic proton in the upper atmosphere or subsequent decays in flight. Those particles are largely absorbed by the lead, while a good fraction of muons -also secondary products of the cosmic ray- pass it rather easily.

Muons are special: they can traverse large amounts of matter with only minor modifications of their momentum. Those that manage to punch through the lead cross a pair of scintillation detectors (read out by photomultipliers labeled A,B at one end), and then enter an aluminum bar (in red). While most muons also exit the aluminum at the bottom, leaving a signal in the other scintillation counter (cyan layer read out by photomultiplier C), a small fraction of them stop in the aluminum, where they stay and then decay with a typical exponential law.

The stopping of muons can be identified by requiring that there is no signal in the lower counter in coincidence with the two on top. This anti-coincidence also determines the start of our clock $t_{start}$ : we want to see how long it takes for the stopped muons to produce an electron, which yields a delayed signal in one of the counters surrounding the aluminum bar (the one labeled C in the plot above). The delayed signal is the stop of the clock $t_{stop}$ : so the time the muon sat in the aluminum bar is simply determined as $\Delta t = t_{stop} - t_{start}$ . Once a stop signal is seen, the value of $\Delta t$ is stored, and the experiment is reset to its initial state, looking for another start. If instead 20 microseconds pass without a stop signal occurring, the event is discarded and the experiment reset, getting ready to catch another stopped muon.

In the experimental setup, you work with a multichannel analyzer (MCA) which converts the time interval $\Delta t$ into a number T from 1 to 256. T=1 corresponds to $20 \mu s /256 = 0.08$ microseconds, while T=256 corresponds to 20 microseconds. Every time a start and a stop occur, the histogram is filled with an entry corresponding to the bin $T=256 \times \Delta t /20 \mu s$ . After a long enough exposure, one should obtain a histogram showing the typical exponential decay law, proportional to the function $N(t)= e^{-t/\tau_\mu}$ , where $\tau_\mu$ is the decay constant, the muon lifetime we want to measure. Are you still with me ? Well, the math is basically over.

Now, all this is nice and simple, but the Devil created backgrounds to keep physicists busy. In fact, during those 20 microseconds while our apparatus waits for the muon to spit out an electron, our counters may be crossed by another particle. This will cause a stop, and the MCA will be filled with a random entry. This entry, it is important to stress, has absolutely no correlation with the arrival time of the muon ( $t_{start}$ ). So we may expect them to be distributed with a totally flat distribution between 0 and 20 microseconds: each one of our 256 channels will be ridden with some background hits, after any given exposure.

Now the question for you is the following. Given that muons produce stops with a falling exponential law - whose decay constant is equal to the muon lifetime (2 microseconds), and thus much smaller than the 20 microseconds during which we keep our system receptive of a stop signal- while background hits produce stops at random times, what is the kind of histogram one expects after a long enough exposure if the overall number of decay and random stops is roughly equal? I give you five options below, and one day to think about it.

A falling exponential distribution with time constant equal to the muon lifetime
A falling exponential distribution with time constant equal to the muon lifetime, on top of a flat distribution
Two falling exponential distributions one on top of each other, one with time constant equal to the muon lifetime, the other with a longer time constant
Two falling exponential distributions one on top of each other, neither of which with a time constant equal to the muon lifetime
A flat distribution

Of course, the problem can be solved analytically, but the math needed is not totally trivial, and I bet you have rather use your intuition. Good luck! Please type your answer in the comment box before reading what others may have answered, to make things more interesting!

Post-scriptum: just before going to bed, I realized that some of the more knowledgeable among you might be misled by the fact that positive and negative muons do quite different things when they stop in aluminum or other materials. Please disregard this detail - or rather, keep it in mind, we will discuss it tomorrow; it has no bearing on the answer of the question I posed above.

The University of Utah have created a web game where you can train as a mad scientist by demonstrating you can label and construct what looks like an alien from a 60s B-movie but is apparently a giant neuron.

For those wanting their mad neuroscientist stereotypes a little stronger, I suggest that the 1985 zombie movie Day of the Dead, where neuroscientists attempt to tame some captured zombies by meddling with their brains in an attempt to work out how to stop the hordes of the undead that are overrunning the earth.

As if you couldn't guess, the neuroscientists turn out to be sadly deluded and become victims of both the zombies and their fellow humans.

There's a moral in there somewhere, but I'm too tired to work it out, so stereotype away.

The feeling I have when deciding what to discuss next about this year’s American Astronomical Society meeting is like what I get in a good used bookstore. Where to turn next? We’ve already looked at several stories with exoplanetary significance, but the arrival of a new type of star entirely seems to vault past even these in significance. If, of course, the so-called ‘quark star’ is real, a question sure to remain controversial as the study of extremely bright supernovae continues.

When I say bright, I’m talking about three events in particular, each of which produced one hundred times more light energy than normal supernovae. The events, designated SN2006gy, SN2005gj and SN2005ap, have been under intense scrutiny, among the researchers a team from the University of Calgary, who point to the lack of a satisfactory explanation of these events. The hypothesis they defended at AAS is that neutron stars are not the most compact solid objects known to exist. That honor belongs to still denser quark stars.

Take an average neutron star, maybe sixteen miles across but 1.5 times as massive as the Sun. Produced by the catastrophic collapse of a massive star (and thus associated with the accompanying supernova explosion), neutron stars could theoretically be packed tighter still, the same mass being squeezed to an object just twelve miles across. At this point, the neutrons dissolve into quarks and vast amounts of energy are unlocked, causing the aforementioned super-luminous events. The researchers — Denis Leahy and Rachid Ouyed — are quick to point out that competing explanations of these supernovae cannot be ruled out without further observations of these exotic phenomena.

All of which is highly speculative but a stunning possibility just the same. What’s happening to the Milky Way itself is also a bit of a surprise, for at the same AAS meeting, a team led by Robert Benjamin (University of Wisconsin, Whitewater) used new imagery from the Spitzer Space Telescope to re-examine the galaxy’s structure. The result: There appear to be not four but just two major arms to our galaxy, a possibility neatly captured in the image below. Benjamin notes how tricky studying a galaxy from within can be:

Image (click to enlarge): Like early explorers mapping the continents of our globe, astronomers are busy charting the spiral structure of our galaxy, the Milky Way. Using infrared images from NASA’s Spitzer Space Telescope, scientists have discovered that the Milky Way’s elegant spiral structure is dominated by just two arms wrapping off the ends of a central bar of stars. Previously, our galaxy was thought to possess four major arms. Credit: NASA/JPL-Caltech.

Earlier radio surveys and the infrared surveys that followed them had revised the initial model of a spiral with four major star-forming arms, but Benjamin’s software has gone to work counting stars and measuring stellar densities, employing a vast Spitzer mosaic that takes in some 110 million stars. The Milky Way now appears to be like other galaxies we have observed with a central bar of stars (the latter a discovery made in the 1990s). The two major arms are now seen to be the Scutum-Centaurus and Perseus arms (although the Perseus arm is not visible in the field of view covered by the new Spitzer images).

The Sagittarius and Norma arms are now considered to be minor, with the Perseus and Scutum-Centaurus arms showing the greatest density of both young, bright stars and older red giants. Bear in mind that our own small star is currently found near the partial arm known as the Orion Spur, located between the Sagittarius and Perseus arms. But as this JPL news release points out, stars tend to move in and out of arms as they orbit the galaxy’s center. In fact, our Sun would have made sixteen circuits of the Milky Way since its formation four billion years ago.

Not long ago we discussed work led by Deena Skolnick Weisberg showing that most people are more impressed by neuroscience explanations of psychological phenomena than plain-old psychology explanations. Talking about brains, it seems, is more convincing than simply talking about behavior, even when the neuroscience explanation doesn't actually add any substantive details.

Now David McCabe and Alan Castel have taken this work on the acceptance of neuroscience to a new level: now they've got pictures! They asked 156 students at Colorado State University to read three different newspaper articles about brain imaging studies. The articles were completely fake, and they all discussed brain imaging, but one of the articles included only text, one included a bar graph showing brain-scan results, and one showed pictures of brains. The articles were about three different topics, but an equal number of students saw each article with text only, the graph, or the brain image.

For example, in one of the fake studies, the claim was made that TV-watching is related to math ability. As evidence, students read a text explanation, or saw one of these two figures:

The [fake] claim was that since the same area of the brain is activated while doing arithmetic or watching TV, that the two activities are related. The students then rated this article for whether its scientific reading made sense, on a scale of 1 (strongly disagree) to 4 (strongly agree). Here are the results:

Engineer Train Engineer - What is the Benefit?
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In a space of 3 months, I taught and trained 2 new graduated chemical engineers about my physical refining plant. As a person in charge of the plant, I thought I already know everything about my plant. However, having those 2 new engineers really tested my real understanding of my own plant.

The two of them were fresh graduates and they haven't seen or experienced being in a plant before. So, they were very curious about the plant, its process, equipments, vessels, PID drawing and a lot more. I explained to them the process involved in the plant step by step with reference to the plant PID drawing. As I went through the initial stage i.e. from the storage tank, then to a pump, strainer, heat exchanger and so on... both of them (in different occasions) tentatively listen and made notes. Then we went through the Niagara filters, filters bags, filter cartridges.... and so on. Then the heating stage... passing through few plate heat exchangers, shell and tube etc. Then to the pack column, another series of heat exchangers, filters and to a product storage tank (I'm not trying to explain my process here, that's just a brief idea on how I explained the steps and processes to them).

The PID (Piping and Instrumentation Diagram) contains all the piping, valves, actuators, pump, RTD, pressure transmitters, level sensors, flow meters, heat exchangers, vessels, strainers, filters and so on in the plant. I need to really look at it and trace the lines in order to show the flow. Well, my plant is quite big and that is really shown in the complicated PID drawing. That's good... at least it refreshes me when I'm explaining to them.

At several occasions, they asked about this and that.... Some of the questions were easy to answer while some others were surprisingly difficult to answer because I forgot, or I don't know about it. That means, there are still points and stuffs about my plant that I haven't covered yet (that I don't know). They made me realized about it and it made me seek for answers. So, after investigating about it and getting answers, I informed those new engineers and indirectly my comprehension on my own plant became better. Looking the scenario at a different angle, I'm actually revising and learning about my own plant when I teach or explain to others. Isn't that cool? Both parties benefited. They learned new process and engineering knowledge while I established my understanding on my plant.

Conclusion and morale of the story...

Don't keep the technical knowledge to yourself. Share them. The more you share, the more you'll get. The more you share, the better you are. The more you share, the stronger you become.

posted at: 11:31 | path: /sci/chem | permanent link to this entry

Wed, 11 Jun 2008

1. Brain training

2. Drugs

3. Nutritional supplements

4. Meditation

5. Exercise

The results are in (for now)