Chemistry posts

July 14, 2010
04:43 AM
760 views

Honey’s anti-bacterial properties found?

New research suggests that (some of) the secrets to honey’s anti-bacterial properties may have been revealed.

A Dutch team of microbiologists propose that the anti-bacterial properties of the honey they tested come down to four chemicals and one general property:

Sugars High concentrations of sugars have long been known to have anti-bacterial properties and are used in preserving food.
The [...]... Read more »

Kwakman, P., te Velde, A., de Boer, L., Speijer, D., Vandenbroucke-Grauls, C., & Zaat, S. (2010) How honey kills bacteria. The FASEB Journal, 24(7), 2576-2582. DOI: 10.1096/fj.09-150789

Xu, P., Shi, M., & Chen, X. (2009) Antimicrobial Peptide Evolution in the Asiatic Honey Bee Apis cerana. PLoS ONE, 4(1). DOI: 10.1371/journal.pone.0004239

Adams, C., Manley-Harris, M., & Molan, P. (2009) The origin of methylglyoxal in New Zealand manuka (Leptospermum scoparium) honey. Carbohydrate Research, 344(8), 1050-1053. DOI: 10.1016/j.carres.2009.03.020

July 12, 2010
03:39 PM
727 views

More Vodka!

by Torah Kachur in Science in Seconds

Molecular differences between vodka brands might confirm what vodka drinkers have long suspected.... Read more »

Hu, N., Wu, D., Cross, K., Burikov, S., Dolenko, T., Patsaeva, S., & Schaefer, D. (2010) Structurability: A Collective Measure of the Structural Differences in Vodkas. Journal of Agricultural and Food Chemistry, 58(12), 7394-7401. DOI: 10.1021/jf100609c

July 12, 2010
10:05 AM
1,237 views

Computational modeling of GPCRs: What are the challenges?

by The Curious Wavefunction in The Curious Wavefunction

GPCRs are extremely important proteins both for pure and applied science research, but they are also very difficult to crystallize and hence structural information on them has been sparse. Naturally in such a case, computational modeling can be expected to be of great value of providing insight into GPCR structure and function. However, even though progress has been impressive, such modeling still has to overcome many challenges. A recent review lists some of them.Firstly, in the absence of crystal structure, homology modeling wherein a sequence for an unknown structure is 'threaded' through that of a known one is well-established as a valuable technique. However the technique is tricky. First and foremost one has to get the right sequence alignment between the target and the template. As the article notes, recent studies have suggested that using multiple structures for alignment instead of a single one provides better results. Particularly noteworthy is this detailed study. Once a homology model has been obtained, it must be meticulously examined, both for internal consistency (bad contacts, incorrect hydrogen bonding interactions etc.) and for its agreement with experiment. Data from cross-linking studies and mutagenesis can be used to achieve this. A recent promising development has been termed 'ligand-supported homology modeling'. In this process, topographical protein-ligand interaction data from mutagenesis and other studies is used to limit the number of homology models. Such data-driven homology modeling is becoming increasingly popular.Once a good homology model has been obtained, many things can be done with it. Molecular dynamics (MD) simulations provide a very valuable avenue for exploring protein motion and be used to detect structural features not obvious in static models. A recent MD simulation of the beta-adrenergic receptor helped to resolve discrepancies between biochemical and structural observations. MD simulations can be used to investigate protein dynamics and to refine the models. Several challenges present themselves during this procedure. Firstly, while helices in GPCRs can be well-modeled, loops (of which there are six- three intracellular and three extracellular) are much harder to model because of their higher flexibility and because they are often ill-resolved in crystal structures. Unfortunately, it's these loops which are important ligand-interacting elements, so getting them right is key. Recently developed algorithms for loop-refinement based on either first-principles energy minimization or by statistical modeling based on a database of known loop conformations have been used in getting loops right. Also, state-of-the-art long MD simulations spanning several microseconds can be used to model large-scale structural changes in GPCRs.There are still immense challenges still to be overcome in understanding GPCRs. One of the biggest concerns the cycling between several inactive and active states (and not just one active and one inactive state) that present often conflicting features that can be subject to varying interpretation. For instance, for class A GPCRs (which is the largest class), it has been well-established that activated states involve the breakage of the "ionic lock", a salt bridge between arginines and glutamates on transmembrane helices 6 and 3. Breaking this lock allows TM6 to shift away from TM3 and towards TM5, a hallmark of GPCR activation. Yet the MD study on the beta2 cited above indicated that even an inactive state may feature breakage of this lock.In the GPCR jungle, strange shape-shifting creatures appear and clutch gems of insight in their palms. It is only fitting that we throw the kitchen sink at them to unravel their secrets, and computational techniques can only be a valuable arrow in this quiver.Yarnitzky T, Levit A, & Niv MY (2010). Homology modeling of G-protein-coupled receptors with X-ray structures on the rise. Current opinion in drug discovery & development, 13 (3), 317-25 PMID: 20443165... Read more »

Yarnitzky T, Levit A, & Niv MY. (2010) Homology modeling of G-protein-coupled receptors with X-ray structures on the rise. Current opinion in drug discovery , 13(3), 317-25. PMID: 20443165

July 7, 2010
12:46 PM
2,831 views

Massive Early Stars And Molecular Hydrogen Cooling

by Joseph Smidt in The Eternal Universe

The big bang produced only Hydrogen and Helium with trace amounts of Lithium. (For the most part.) This is a problem for star formation because stars need to be "cool" to form and typically you need heavier elements to help the star cool off. This is why:

Gravity pulls mass together. However, as matter gets pulled together it heats up and this heat causes the matter to want to expend again.

... Read more »

Kreckel, H., Bruhns, H., Cizek, M., Glover, S., Miller, K., Urbain, X., & Savin, D. (2010) Experimental Results for H2 Formation from H- and H and Implications for First Star Formation. Science, 329(5987), 69-71. DOI: 10.1126/science.1187191

July 7, 2010
03:10 AM
310 views

Some like it flat. Flat carbon flat nitrogen

by Rik in NNNS chemistry blog

We are quite used to a trigonal pyramidal molecular geometry for trivalent nitrogen and a tetrahedral one for tetravalant carbon. It does not have to be that way. For example in the compound class of the fenestranes the central carbon atom is flattened and so is the central nitrogen atom in triisopropylamine. Two recent publications describe more flattening: of nitrogen and of carbon.
... Read more »

Jie, Y., Livant, P., Li, H., Yang, M., Zhu, W., Cammarata, V., Almond, P., Sullens, T., Qin, Y., & Bakker, E. (2010) An Acyclic Trialkylamine Virtually Planar at Nitrogen. Some Chemical Consequences of Nitrogen Planarity. The Journal of Organic Chemistry, 75(13), 4472-4479. DOI: 10.1021/jo100628v

Cooper, O., Wooles, A., McMaster, J., Lewis, W., Blake, A., & Liddle, S. (2010) A Monomeric Dilithio Methandiide with a Distorted trans-Planar Four-Coordinate Carbon. Angewandte Chemie International Edition. DOI: 10.1002/anie.201002483

Chemistry

July 1, 2010
02:32 PM
1,658 views

Are Zombie Vultures In Our Future?

by GrrlScientist in Living the Scientific Life (Scientist, Interrupted)

A zombie is another name for The Walking Dead -- those who are lifeless, apathetic, or totally lacking in independent judgment. But in an ecological sense, a zombie species no longer fulfills its ecological function because it is becoming extinct... Read more »

Shultz, S., Baral, H., Charman, S., Cunningham, A., Das, D., Ghalsasi, G., Goudar, M., Green, R., Jones, A., Nighot, P.... (2004) Diclofenac poisoning is widespread in declining vulture populations across the Indian subcontinent. Proceedings of the Royal Society B: Biological Sciences, 271(Suppl_6). DOI: 10.1098/rsbl.2004.0223

Lemus, J., & Blanco, G. (2009) Cellular and humoral immunodepression in vultures feeding upon medicated livestock carrion. Proceedings of the Royal Society B: Biological Sciences, 276(1665), 2307-2313. DOI: 10.1098/rspb.2009.0071

Naidoo, V., Wolter, K., Cromarty, D., Diekmann, M., Duncan, N., Meharg, A., Taggart, M., Venter, L., & Cuthbert, R. (2009) Toxicity of non-steroidal anti-inflammatory drugs to Gyps vultures: a new threat from ketoprofen. Biology Letters, 6(3), 339-341. DOI: 10.1098/rsbl.2009.0818

Jackson, A., Ruxton, G., & Houston, D. (2008) The effect of social facilitation on foraging success in vultures: a modelling study. Biology Letters, 4(3), 311-313. DOI: 10.1098/rsbl.2008.0038

Swan, G., Cuthbert, R., Quevedo, M., Green, R., Pain, D., Bartels, P., Cunningham, A., Duncan, N., Meharg, A., Lindsay Oaks, J.... (2006) Toxicity of diclofenac to Gyps vultures. Biology Letters, 2(2), 279-282. DOI: 10.1098/rsbl.2005.0425

Cuthbert, R., Parry-Jones, J., Green, R., & Pain, D. (2007) NSAIDs and scavenging birds: potential impacts beyond Asia's critically endangered vultures. Biology Letters, 3(1), 90-93. DOI: 10.1098/rsbl.2006.0554

June 30, 2010
09:00 AM
643 views

Jumping Genes, Taking Names

by Brit Trogen in Science in Seconds

Everyone wants to stand out in the crowd. And thanks to new findings independently reported by three labs in this week’s Cell, we all might be a lot more unique than we thought.

The identity-inducing culprit? Everyone’s favorite jumping genes: transposons. Yes, the genes that just can’t sit still—the same ones Barbara McClintock owes a large part of her fame to—are making a comeback in a major way. Because what self-respecting gene wants to wait for that lumbering, sloth-like beast we call evolution to be passed around from place to place? Chromosomal cross-overs are so last season. All the cool genes are getting active.

Transposons are believed to make up an astounding 50% of the genetic material of all humans, and while it was previously estimated that new transpositions occurred in about one in every 20 live births, this new research is showing that the number is much more frequent. Every baby born likely has a transposition that is completely unique.

What’s the significance of this new finding? Well, cancer, for one. While most transposons move around the genome “silently,” without causing any noticeable effect in the biology of the host, some can jump into oncogenes like those responsible for tumor suppression, unlocking key steps in the progression of the disease. Now that we are beginning to understand the extent of their activity, we can at least glean a better picture of the true role they may be playing in these types of mutations.

Yeah, yeah, I know. Everyone and their lab tech pulls the C card. But when your project is literally half of the genome, I guess we can give you a pass.

Iskow, R., McCabe, M., Mills, R., Torene, S., Pittard, W., Neuwald, A., Van Meir, E., Vertino, P., & Devine, S. (2010). Natural Mutagenesis of Human Genomes by Endogenous Retrotransposons Cell, 141 (7), 1253-1261 DOI: 10.1016/j.cell.2010.05.020 Lupski, J. (2010). Retrotransposition and Structural Variation in the Human Genome Cell, 141 (7), 1110-1112 DOI: 10.1016/j.cell.2010.06.014 Beck, C., Collier, P., Macfarlane, C., Malig, M., Kidd, J., Eichler, E., Badge, R., & Moran, J. (2010). LINE-1 Retrotransposition Activity in Human Genomes Cell, 141 (7), 1159-1170 DOI: 10.1016/j.cell.2010.05.021
... Read more »

Iskow, R., McCabe, M., Mills, R., Torene, S., Pittard, W., Neuwald, A., Van Meir, E., Vertino, P., & Devine, S. (2010) Natural Mutagenesis of Human Genomes by Endogenous Retrotransposons. Cell, 141(7), 1253-1261. DOI: 10.1016/j.cell.2010.05.020

Lupski, J. (2010) Retrotransposition and Structural Variation in the Human Genome. Cell, 141(7), 1110-1112. DOI: 10.1016/j.cell.2010.06.014

Beck, C., Collier, P., Macfarlane, C., Malig, M., Kidd, J., Eichler, E., Badge, R., & Moran, J. (2010) LINE-1 Retrotransposition Activity in Human Genomes. Cell, 141(7), 1159-1170. DOI: 10.1016/j.cell.2010.05.021

June 29, 2010
05:34 AM
759 views

The chemistry of an iPhone

by David Bradley in Reactive Reports Chemistry Blog

Apple’s Steve Jobs has a reputation for responding personally to some of the presumably millions of emails he receives. (Apparently, he does it on a weekly basis, which smacks of controlled PR campaign, if you ask me). One from “Derick” published on Wired and elsewhere purportedly asked about the chemistry of the iPhone 4. Derick [...]... Read more »

Rosman, K., Chisholm, W., Hong, S., Candelone, J., & Boutron, C. (1997) Lead from Carthaginian and Roman Spanish Mines Isotopically Identified in Greenland Ice Dated from 600 B.C. to 300 A.D. . Environmental Science , 31(12), 3413-3416. DOI: 10.1021/es970038k

Chemistry

June 28, 2010
08:26 PM
782 views

Can oil and water mix?

by Michael Clarkson in Conformational Flux

We all know that linear polymers of amino acids (proteins) adopt complex three-dimensional structures when they are dissolved in water. The process of forming these structures is called folding, and it is understood to occur because proteins are amphiphilic. Some parts of a protein chain like to interact with water (hydrophilic), while others are oily and want to get out of water (hydrophobic). Folding of the chain sticks all the oily parts together on the inside of the structure while the parts of the chain that have favorable interactions with water remain on the outside. An upcoming paper from the Journal of Physical Chemistry B suggests that sufficiently long alkanes might undergo a similar transition, even though they don't have any chemical groups that like to interact with water.
Researchers from Purdue University performed a variety of simulations of linear alkanes, which are saturated hydrocarbon chains typically designated by the number of carbons they contain (C8 has 8 carbons). Because increasing the length of the chain just involves inserting an identical unit, one might expect that after a certain point the properties of these molecules would scale linearly with chain length. Previous experiments and simulations, however, indicated that the free energy of hydration did not match this predicted linear trend. Instead, the free energy of hydration (ΔG) remained flat or even decreased as chain length increased. This is because the linear extrapolation only holds for a chain that adopts a linear (all-trans) configuration. As chain length increases, however, the numerous additional degrees of freedom allow a chain to adopt a more compact conformation that decreases the penalty incurred by interacting with water.

Given this understanding, the authors asked whether a sufficiently long alkane might be hydrophilic. They established theoretical bounds for this question by examining two extreme possibilities. In the first, the alkane was assumed to be linear, and of course the ΔG never crossed zero. In the second, the alkane was assumed to collapse into a sphere; in this case the free energy becomes favorable after less than a dozen carbons. Presumably the reality lies somewhere in between. To get a more realistic view of the situation, they also simulated the behavior of alkane chains of various sizes, and found that the potential energy released by dissolving an alkane in water was correlated with the solvent-accessible surface area (SASA). From their findings they predicted that an alkane that was long enough would eventually cross over into hydrophilicity.

This is pretty interesting, but there are some significant weaknesses. The work here is purely theoretical and uses a molecular dynamics forcefield with an imperfect model of water behavior. The predictions of this simulation have been validated on real-world chemical samples, but only up to a comparatively modest chain length of C16. Because the surprising prediction lies very far from the zone of simulations that have been experimentally confirmed, one could argue that this is all just an extended discussion of a failure of the model. As they are aware of this shortcoming, the authors performed a number of simulations on collapsed (globular) C100 alkanes in order to determine the energy of the interaction between the chain and water, as well as the SASA. They found that, within error, the simulated values also indicated a negative ΔG of hydration.

As the authors note, this result doesn't necessarily mean that you could dissolve a whole bunch of C100 in water. The ΔG calculated here is for transfer from the gas phase into water, and C100 is unlikely to be highly volatile, given that all the higher paraffins are solids. In addition, these are simulations of a single hydrocarbon chain in water, and so they don't tell us about the energetics of lipid-lipid interactions. Oils segregate out of water because the oil-oil interaction is more favorable than the oil-water interaction. If this holds true for C100, even if dissolving in water is itself favorable, the alkane will still form oil droplets more readily than it will dissolve.

Using their models, the authors predict that the surface tension of an oil droplet will be negative at chain lengths greater than C50, thus tending to release oil into solution, but I'm a bit worried about this prediction. First, the molecular surface tensions are very far off from the macroscopic tensions, indicating that this calculation misses a great deal about the interaction. In addition, they perform a linear extrapolation from the approximately linear tail of what appears to be an exponential curve. It's not clear why this extrapolation was used or what conclusions can be drawn from it; the molecular surface tension could just as easily be asymptotic with respect to the zero point.

Experimental verification of this prediction is unlikely to appear any time soon, if at all. Synthesizing very long alkanes is not trivial, especially in the quantities and purities required to put these simulations to the test. Ultimately the value of a paper like this is not in any practical application, but rather in the fact that it reminds us of the strength of the forces that guide the formation of higher-order chemical structures. Even in the absence of any group that has an intrinsically favorable interaction with water, the energy released by self-binding of hydrophobic groups may give rise to a "folded" structure for very long alkanes.

Underwood, R., Tomlinson-Phillips, J., & Ben-Amotz, D. (2010). Are Long-Chain Alkanes Hydrophilic? The Journal of Physical Chemistry B DOI: 10.1021/jp912089q... Read more »

Underwood, R., Tomlinson-Phillips, J., & Ben-Amotz, D. (2010) Are Long-Chain Alkanes Hydrophilic?. The Journal of Physical Chemistry B, 2147483647. DOI: 10.1021/jp912089q

June 28, 2010
11:40 AM
659 views

Azobenzene photoswitching in vesicles

by Lars Fischer in EuCheMS 2010 Blog

The photoswitching capability of azobenzenes has recently been used extensively in photoreactive supramolecular materials. One of the most astonishing uses of azonenzene photoswitching is the reversible association of these molecules with certain cyclodextrines. Azobenzenes change their structure reversibly under irradiation. There’s a cis-form and a trans-form, and photoisomerisation happens reliably wavelengths of 350 (trans –> [...]... Read more »

Nalluri, S., & Ravoo, B. (2010) Lichtgesteuerte molekulare Erkennung und Adhäsion von Vesikeln. Angewandte Chemie. DOI: 10.1002/ange.201001442

June 23, 2010
04:10 PM
683 views

Sustainability of Organic vs Synthetic Pesticides

by Michael Long in Phased

Rebecca Hallett (University of Guelph, Canada) and coworkers show that organic pesticides can be less toxic to soybean aphid insect pests, more toxic to beneficial insects, and more environmentally harmful than synthetic pesticides. This news feature was written on June 23, 2010.... Read more »

Bahlai, C. A., Xue, Y., McCreary, C. M., Schaafsma, A. W., & Hallett, R. H. (2010) Choosing Organic Pesticides over Synthetic Pesticides May Not Effectively Mitigate Environmental Risk in Soybeans. PLoS ONE, 5(6). DOI: 10.1371/journal.pone.0011250

June 22, 2010
07:00 AM
636 views

Seeing Molecules

by Rheanna Sand in Science in Seconds

In this video, Science in Seconds looks at the world's first picture of a molecule, taken by IBM researchers in 2009 and published in Science Magazine.... Read more »

Gross, L., Mohn, F., Moll, N., Liljeroth, P., & Meyer, G. (2009) The Chemical Structure of a Molecule Resolved by Atomic Force Microscopy. Science, 325(5944), 1110-1114. DOI: 10.1126/science.1176210

June 21, 2010
05:00 AM
1,404 views

Does Lipid Overload Cause Diabetes?

by Steve Parker, M.D. in Diabetic Mediterranean Diet Blog

An up-and-coming theory to explain type 2 diabetes suggests that abnormal lipid metabolism, not glucose/sugar metabolism, is the primary metabolic defect. Roger H. Unger, M.D., wrote about this in the March 12, 2008, issue of the Journal of the American Medical Association. Early in the writing of this blog entry, I realized it is much [...]... Read more »

Unger, R. (2008) Reinventing Type 2 Diabetes: Pathogenesis, Treatment, and Prevention. JAMA: The Journal of the American Medical Association, 299(10), 1185-1187. DOI: 10.1001/jama.299.10.1185

June 20, 2010
11:47 AM
980 views

Looking at your statistical models...

by egonw in Chem-bla-ics

I do not think I have ever blogged the paper that played an important role in my thesis (doi:10.1021/ci990038z); research of one of the papers in my thesis, started with the hypothesis proposed therein. The paper had a really good idea; but, unfortunately, it did not contain the data to support the hypothesis. That gets me to one important lesson I learned: a QSAR data set of less than 100 molecules is not enough to make untargeted statistical models.

The paper reads quite nicely, and the results are clear: by combining spectral types, the RMSEP goes down. Good! Lower prediction errors; that's what we all want. So, a M.Sc. student of mine set off, but after about half a year, he was still unable to make statistically good models. He used bootstrapping to 'prove' it was not his fault: there was not enough data for the method to learn the underlying patters. Hence my above lesson. My student went on with larger data sets, and laid out the foundation of what later became the paper on using NMR spectra in QSPR modeling (doi:10.1021/ci050282s). Now you understand why QSAR is missing.

So, if those results are so clear, then why does it not work? As said, the data set was too small for pattern recognition methods to see what was going on. The RMSEP numbers just came out nicely; however, if we had only made the below plot, if would have warned. But I failed to do that at the time. Lesson learned: do not just look at the data, but also look at the model. And look really means looking with your eyes at graphical representations of that model. The plot:

The numbers in this plot are hidden in tables in the paper. The RMSEP values earlier mentioned are calculated from those. From the plot, you can see that the test data consisted of 5 compounds; the training set contained 37 compounds; all are congenerics, and do not span a high diversity. Now, the plot shows five models: black is COMFA; orange is based on experimental IR spectra; red, green, and blue are models where two types of representations are combined. From the RMSEP values it can be seen that combining representation improves the RMSEP values. That's what you want, and sort of makes sense.

Now, I did not make this plot until I started writing up the paper, and tried to figure out why the QSAR data set did not work. My eyes opened wide when I saw the orange dots! Anti-correlation! WTF?!?! I mean, we are looking at a plot visualizing the predicted versus the experimental activity... Actually, the others are not really convincing either, are they? Looking at the predictions for compounds with experimental values around 1.0-1.5 (if you really want to know the unit, read the article), the pattern is pretty much anti-correlated too. Thinking about it, it seems the RMSEP is mainly reflecting the error of the left most compound, the one with a experimental activity of about 0.3.

Clearly, the orange model is hopeless, but the others are not really better. Now, the paper actually makes statements comparing the various combinations of representations, but, in retrospect and looking at this plot, I wonder if the green model is really different from the blue or red models.

Since then, I always make these kind of plots, just to see what my model is like. Since then, I distrust papers that only show RMSEP, Q2, or other quality statistics. Now, the tricky part is, you need those statistics if you want to automate model selection; the variance on those model quality statistics is actually so high (see also my other post today), that you must carefully validate that model selection too, visually of course.

I have been long thinking what to do with these observations. I did not dare publish them in my thesis; I did not dare write a letter to the editor. Perhaps I should. But even writing up this blog makes me feel uncomfortable. Besides the fact that I might be wrong, I also do not like to point out mistakes (IMHO); particularly, when those are published in a respectable journal. I was fooled by the statistics too (and was already well trained), so I cannot comment on the authors overlooking the issue. Or the reviewers! Or the community at large. Also, I do not know what the fate of this paper should be. The idea is quite interesting, even though the published results do not support it. Not shown here, but the bootstrapping results show that the apparent slight improvement is merely a numerical artifact, just happening by chance, based on luckily selecting the test compounds; the data is just insufficient in size to draw any conclusion.

Comparative Spectra Analysis (CoSA): Spectra as Three-Dimensional Molecular Descriptors for the Prediction of Biological Activities Journal of Chemical Information and Modeling, 1999, 39 (5), 861-867 DOI: 10.1021/ci990038z
Willighagen, E., Denissen, H., Wehrens, R., & Buydens, L. (2006). On the Use of 1H and 13C 1D NMR Spectra as QSPR Descriptors Journal of Chemical Information and Modeling, 46 (2), 487-494 DOI: 10.1021/ci050282s... Read more »

Bursi, R., Dao, T., van Wijk, T., de Gooyer, M., Kellenbach, E., & Verwer, P. (1999) Comparative Spectra Analysis (CoSA): Spectra as Three-Dimensional Molecular Descriptors for the Prediction of Biological Activities. Journal of Chemical Information and Modeling, 39(5), 861-867. DOI: 10.1021/ci990038z

Willighagen, E., Denissen, H., Wehrens, R., & Buydens, L. (2006) On the Use of H and C 1D NMR Spectra as QSPR Descriptors . Journal of Chemical Information and Modeling, 46(2), 487-494. DOI: 10.1021/ci050282s

June 18, 2010
03:44 PM
448 views

Chemical Evidence that Phar Lap was Poisoned with Arsenic

by Michael Long in Phased

Ivan Kempson (Ian Wark Research Institute, Australia) and Dermot Henry (Museum Victoria, Australia) have formally presented the first conclusive chemical evidence that Phar Lap, a famous race horse, was poisoned with arsenic. This news feature was written on June 18, 2010.... Read more »

Kempson, I. M., & Henry, D. A. (2010) Determination of Arsenic Poisoning and Metabolism in Hair by Synchrotron Radiation: The Case of Phar Lap. Angewandte Chemie International Edition, 49(25), 4237-4240. DOI: 10.1002/anie.200906594

June 18, 2010
09:47 AM
1,020 views

Effect of Plastics in Fauna (particularly marine fauna)

by Sarah Stephen in Our Gossamer Planet

We may not be strangers to scary sights, photographs, and news: a cow and its calf chewing a plastic bag in rural Kerala whilst the daily newspaper cites the number of animals which have died after ingesting plastic bags and wastes discarded by the roadsides, a little puppy being trapped in that suffocating cloak, and a turtle inquisitively approaching a plastic bag. Although I have already referred to the plastic menace in this blog, as well as while highlighting David de Rothschild’s Plastiki in Ecoratorio, this post will focus on the effect of plastics in fauna (particularly marine).Plastics in marine environment Due to the increased production (the plastic resin production increased 25-fold from 1960 to 2000) and use of plastics, it is hardly surprising that there is a corresponding increase in the quantity of plastic waste entering the marine environment. In fact, 10% of the approximately 100 million tonnes of plastic estimated to be produced per annum have ended up in the marine environments. As a result, 60-80% (90-95% in some areas) of total marine pollution is due to plastics. And this common and persistent pollution has its consequences.Effects in marine fauna: overviewPlastics result in the injury and deaths of hundred thousands of marine fauna per annum (or more, for it is impractical to accurately calculate the number of affected animals in all marine environments), including crustaceans, fishes, dolphins, whales, turtles, seals, and seabirds. As of yet, 267 species of marine organisms worldwide are known to have been affected by plastic debris, a number which is bound to increase after factoring in smaller marine organisms. The fate of all these marine species is hanging in a balance given that they already face other threats to their existence, most notably by other anthropogenic activities. For instance, derelict and/or lost fishing nets have resulted in the deaths of an innumerable number of fishes, birds, and mammals after these get entangled.What are the threats posed by these plastic bags?i. Plastics can entangle the marine fauna, oft drowning them or impairing their ability to catch food or avoid predators. It is also very possible that the fauna could be wounded by the abrasive debris.ii. Fauna mistake plastic bags and other disintegrated pieces as food. And the consequences are appalling, with either any or all of the following happening: strangulation, suffocation, abrasions/wounding, poisoning (polychlorinated biphenyls are absorbed), and blockages in the alimentary canal. It is very likely that normal feeding and digestion and/or respiration would be hindered, thus resulting in starvation. And the future for these affected fauna certainly seems bleak.Examples1. SharksThree juvenile Brazilian sharpnose sharks (Rhizoprionodon lalandii), were found off the coast of southeast Brazil, with plastic debris rings around their gill or mouth region. The rings had caused severe abrasion, which probably amplified when the fish grew. Given the emaciated state of two sharks, it is likely that the collars (identified as detachable lid parts of plastic bottles) probably hampered normal feeding and/or ventilation (study by Sazim et al, of Universidade Estadual de Campinas and Universidade Santa Cecília, 2002).2. Turtles The already endangered/threatened turtles mistakenly consider the floating semi-transparent bags as jellyfishes (their primary food), and thus ingest plastic bags, fishing lines, and other plastics. Autopsied turtles have revealed plastic bags in their stomachs, with one notable case off Hawaii turning up around 1000 pieces of plastic, including part of a comb, a toy truck’s wheel, and a nylon rope.3. Birds44% of all marine bird species are known to ingest plastic. One study demonstrated ingestion of plastic in 36 out of 60 seabird species sampled off southern Africa. Albatrosses, fulmars, and procellariiforms mistake floating plastics for food or fishes. At Midway Island, out of the 500,000 albatross chicks born each year, 200,000 perish mainly due to consuming plastic fed to them by their parents.Out of a sample of seven red phalaropes (Phalaropus fulicarius), collected from a flock of 6000 late spring migrants, six stomachs were found to contain plastic particles by Peter Connors and Kimberly Smith (1982), of the University of California at Berkeley.Plastic was most frequently seen in procellariiforms (notably Blue Petrels, Pintado Petrels, White-faced Storm-petrels, and Great Shearwaters). Blue petrel chicks at the remote Marion Island, off South Africa, showed that 90% of the examined chicks had plastic in their stomachs, again apparently having been fed these by their parents. In another study, the mass of ingested plastic in Great Shearwaters was positively correlated with PCBs in their fat and eggs. When University of Cape Town’s Bridget Furness (1983) sampled bird species in the Benguela Current, small plastic particles were found in White-chinned Petrels and Great Shearwaters. Peter Ryan, also of UCT, established that the size of ingested particles was related to body size, and this affected the proportions of plastic types ingested. Convincing evidence also indicated that although birds generally chose darker-coloured particles over paler particles, the smaller species of birds were less colour-selective and thus correspondingly exhibited a higher incidence of plastic ingestion than the larger species. The incidence of ingested plastic was directly related to foraging technique and was inversely related to the frequency of egestion of indigestible stomach contents. In one of the sampled species, secondary ingestion of plastic through the contaminated prey was important.4. Cetaceans 26 species of cetaceans accidentally ingest plastic bags, fishing lines and other plastics, which is very much exacerbated when they swallow large mouthfuls of water during feeding. The recent autopsy of a 37-foot long gray whale (in mid April 2010), which came ashore at Arroyo Beach near Seattle, revealed a stomach full of fresh trash, including sweatpants, a golf ball, surgical gloves, duct tape, small towels, bits of plastic, and more than 20 plastic bags. Since these whales are bottom feeders, it is likely that they would unknowingly ingest in these garbage which may have sedimented at the bottom.Some whale species (such as the reclusive Beaked whale, one of which washed ashore on the Isle of Mull, off the West Coast of Scotland) swallow plastic bags mistaking these for their favourite food, the squid. When the Isle of Mull whale was autopsied, its stomach was seen to contain 23 plastic bags and fragments (some being large dustbin liners and supermarket types).5. Other The same pattern is seen amongst the terrestrial fauna. A recent example is that of Whitey, a 10-foot long crocodile in Australia, which died after being relocated to the popular tourist destination of Magnetic Island. It had consumed 25 plastic shopping bags, garbage bags, a plastic wine cooler bag, and a rubber float.A legitimate concern?Obviously. The above examples are valid evidences. Furthermore, the gravity of the situation and the extent of the pollution are well exemplified when considering that the beaked whale, which feeds 100-200 miles off shore, had a stomach filled with plastic. And the carcharhinid shark species (Sazim et al, 2002) face a great risk since they dwell and reproduce in shallow waters.Food web and BioaccumulationFurthermore, the effect of these pollutants doesn’t end with the demise of the affected animal. The ingested plastics (being non-biodegradable and takes a few good centuries to degrade) remains intact, even after the decomposition of the victim, until it becomes the bane of another animal. The accumulation of plastic debris on the sea floor can also inhibit gas exchange, and disrupt and/or smother the benthic fauna.In yet another twist (Mato et al, 2001), the floating plastic fragments and pieces acts as sponges, adsorbing hydrophobic pollutants (such as PCBs, nonylphenols, and DDE), and significantly and steadily accumulating these to a high magnitude of concentration. These micro-debris (marine plastic debris Given that some of the cited researches were conducted during 1982-1... Read more »

Ryan, P. (1987) The incidence and characteristics of plastic particles ingested by seabirds. Marine Environmental Research, 23(3), 175-206. DOI: 10.1016/0141-1136(87)90028-6

Mato, Y., Isobe, T., Takada, H., Kanehiro, H., Ohtake, C., & Kaminuma, T. (2001) Plastic Resin Pellets as a Transport Medium for Toxic Chemicals in the Marine Environment. Environmental Science , 35(2), 318-324. DOI: 10.1021/es0010498

DERRAIK, J. (2002) The pollution of the marine environment by plastic debris: a review. Marine Pollution Bulletin, 44(9), 842-852. DOI: 10.1016/S0025-326X(02)00220-5

Sazima I, Gadig OB, Namora RC, & Motta FS. (2002) Plastic debris collars on juvenile carcharhinid sharks (Rhizoprionodon lalandii) in southwest Atlantic. Marine pollution bulletin, 44(10), 1149-51. PMID: 12474977

MOORE, C. (2008) Synthetic polymers in the marine environment: A rapidly increasing, long-term threat. Environmental Research, 108(2), 131-139. DOI: 10.1016/j.envres.2008.07.025

June 18, 2010
05:11 AM
493 views

A catalyst for axial chirality

by Lars Fischer in EuCheMS 2010 Blog

Axial-chirality or atropisomerism is a very useful property as demonstrated by various chiral catalysts containing BINOL, BINAP and similar groups, but not only there. Many important natural products like e.g. the antibiotic Vancomycin are also atropisomers, which makes this property a very important aspect of stereoselective chemical synthesis. Which is extremely difficult to achieve synthetically [...]... Read more »

Gustafson, J., Lim, D., & Miller, S. (2010) Dynamic Kinetic Resolution of Biaryl Atropisomers via Peptide-Catalyzed Asymmetric Bromination. Science, 328(5983), 1251-1255. DOI: 10.1126/science.1188403

Chemistry

June 18, 2010
05:00 AM
983 views

Sugar-Sweetened Beverages: Bane of Mankind?

by Steve Parker, M.D. in Diabetic Mediterranean Diet Blog

Over the last 30 years in the U.S., consumption of sugar-sweetened beverages (SSBs) has increased from3.9% of total calories to 9.2% (in 2001). In that same time span, the percentage of overweight American adults increased from 47% to 66%. The obesity percentage rose from15 to 33% of adults. [Did the beverages cause the weight gain, or [...]... Read more »

Malik, V., Popkin, B., Bray, G., Despres, J., & Hu, F. (2010) Sugar-Sweetened Beverages, Obesity, Type 2 Diabetes Mellitus, and Cardiovascular Disease Risk. Circulation, 121(11), 1356-1364. DOI: 10.1161/CIRCULATIONAHA.109.876185

June 16, 2010
07:00 PM
438 views

A Biochemical Assay that May Help Predict Adolescent Suicide Risk

by Michael Long in Phased

Damir Janigro (Cleveland Clinic, United States) and coworkers have developed a biochemical assay that may help predict the risk of suicide among psychotic adolescents, enabling preventive care to be focused on those most in need of early intervention. This news feature was written on June 16, 2010.... Read more »

Falcone, T., Fazio, V., Lee, C., Simon, B., Franco, K., Marchi, N., & Janigro, D. (2010) Serum S100B: A Potential Biomarker for Suicidality in Adolescents?. PLoS ONE, 5(6). DOI: 10.1371/journal.pone.0011089

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