Henüz televizyonun içindeki renkli şeylerin gerçek bir ''iş'' olduğunu fark edemeyecek kadar küçük bir yaştan itibaren hep ilgim vardı o kara kutunun içindekine. Herkes gibi bir seyirci olduğumu zannediyordum aslında başlarda. Her Türkiyeli genç gibi ben de istediğim işe çoktan seçmeli bir test sonucunda sahip olabileceğimi zannediyordum. Öyle olmadığını kısa zaman sonra anladım. Üniversite hayatımın bana ve gelecek planlarıma etkisini anlatmayacağım bu yazının konusu bu değil. Ama bir şekilde farklı yollardan geçerek de olsa istediğim, merak ettiğim konuları öğrenebildiğim dersler alabildim. Ağzından çıkanları pür dikkat dinlediğim hocalarım da oldu. Ben büyürken (her manada) aslında en başından beri televizyona bir iletişimci olarak yaklaştığımı fark ettim. Mesleğim için alt yapımı çok küçük yaşlardan beri oluşturmaya başlamışım meğer. Birilerine bir şeyler anlatabilmek-ti bütün derdim. Bana iyi gelen herkese iyi gelsin, paylaşalım hayatı istiyordum. En basit haliyle hikayeler anlatmak istiyordum işte.
Ama ne yazık ki sadece dört yıllık süreç içinde, ben geliştikçe, daha çok düşündükçe, daha çok gördükçe, daha çok okudukça televizyonun içindeki her şey benden daha uzağa savruldu.
****
Milyonlara aynı anda ulaşan bir şey televizyon. Değiştirme, dönüştürme gücü çok yüksek bir araç.
Farklılığa zemin sağlayabilecekken aynılığa itiyor herkesi.
Aynılık ve derin bir aptallık.
Ben, fikrine duruşuna, alt yapısına bizleri yönlendirişine minnet duyduğum kimselerle bu konuları tartışırken, değişen kanal yapıları beni üzüyor.
Hep aynı kişilerin katıldığı tartışma programları, habercilik ahlakıyla uzaktan yakından ilgisi olmayan, birilerinin basın ve halkla ilişkiler departmanı olmuş haber programları, 200 dakikayı bulmuş süresi uzadıkça içerik kalitesi bozulmuş, aptal hikayelerle doldurulmuş televizyon dizileri, magazin programı adı altında doğallık kisvesiyle saçma sapan bir üslup takınmış bir takım insanlar ... ve bunların ucunda inanılmaz paralar ve siyasi çıkarlar.
Ne yapmalıyım bilmiyorum, elimde olan tek şey sosyal medya araçlarımla anlatmaya çalışmak.
Televizyon karşısında ya millet bunları izliyor işte deyip mutfağa geçtiğinde bir tepki göstermiş olmuyorsun.
Sana dayatılan bu aptallığa bir ses çıkar.
Seni uyuşturarak dünyanın parasını kazanıyor bu insanlar. Hepinizin gözünün içine baka baka anlatmak istiyorum.
Tek tek, tane tane.
Sokak sadece sandık başında değişmedi, değişmiyor.
O programlardaki kadınların ve adamların dili değiştirdi sokağı.
Evlenmek için maaş sorduğunda o kadın değişti sokak.
Haber kanalları doğruyu değil istenileni anlattığında değişti sokak.
Kanallar arasındaki keskin-siyasi- ayrımların da yardımıyla değişti sokak.
Her hafta birilerinin ölüp öldürüldüğü diziye değilde birbirini seven iki insanın öpüşmesine ceza kesildiğinde değişti sokak.
Sokak evlerin salonundan başladı değişmeye.
Lütfen artık uyanalım, uyanın.
İzlemezsen, sorarsan, reddedersen, ses çıkarırsan
Sen istersen olur uyan artık lütfen
18 Mart ÇANAKKALE GEÇİLMEZZ...
Bomba şimşekleri beyninden inip her siperin
Sönüyor göğsünün üstünde o arslan neferin.
Ölüm indirmede gökler, ölü püskürmede yer,
O ne müthiş tipidir, savrulur enkazı beşer.
Boşanır sırtlara, vadilere, sağnak sağnak.
Kafa göz, gövde, bacak, kol, çene, parmak, el ayak
Vurulup, tertemiz alnından, uzanmış yatıyor,
Bir hilal uğruna yarap ne güneşler batıyor.
Ey bu topraklar için toprağa düşmüş asker
Gökten ecdat inerek öpse o pak alnı değer.
Sana dar gelmeyecek makberi kimler kazsın?
Gömelim gel seni tarihe desem sığmazsın.
M.Akif ERSOY
The statistics of Promissory Science. Part I: Making non-sense with statistical methods
Statistics is a form of mathematics, a way devised by humans for representing abstract relationships. Mathematics comprises axiomatic systems, which make assumptions about basic units such as numbers; basic relationships like adding and subtracting; and rules of inference (deductive logic); and then elaborates these to draw conclusions that are typically too intricate to reason out in other less formal ways. Mathematics is an awesomely powerful way of doing this abstract mental reasoning, but when applied to the real world it is only as true or precise as the correspondence between its assumptions and real-world entities or relationships. When that correspondence is high, mathematics is very precise indeed, a strong testament to the true orderliness of Nature. But when the correspondence is not good, mathematical applications verge on fiction, and this occurs in many important applied areas of probability and statistics.
You can't drive without a license, but anyone with R or SAS can be a push-button scientist. Anybody with a keyboard and some survey generating software can monkey around with asking people a bunch of questions and then 'analyze' the results. You can construct a complex, long, intricate, jargon-dense, expansive survey. You then choose who to subject to the survey--your 'sample'. You can grace the results with the term 'data', implying true representation of the world, and be off and running. Sample and survey designers may be intelligent, skilled, well-trained in survey design, and of wholly noble intent. There's only one little problem: if the empirical fit is poor, much of what you do will be non-sense (and some of it nonsense).
Population sciences, including biomedical, evolutionary, social and political fields are experiencing an increasingly widely recognized crisis of credibility. The fault is not in the statistical methods on which these fields heavily depend, but in the degree of fit (or not) to the assumptions--with the emphasis these days on the 'or not', and an often dismissal of the underlying issues in favor of a patina of technical, formalized results. Every capable statistician knows this, but of course might be out of business if openly paying it enough attention. And many statisticians may be rather disinterested or too foggy in the philosophy of science to understand what goes beyond the methodological technicalities. Jobs and journals depend on not being too self-critical. And therein lie rather serious problems.
Promissory science
There is the problem of the problems--the problems we want to solve, such as in understanding the cause of disease so that we can do something about it. When causal factors fit the assumptions, statistical or survey study methods work very well. But when causation is far from fitting the assumptions, the impulse of the professional community seems mainly to increase the size, scale, cost, and duration of studies, rather than to slow down and rethink the question itself. There may be plenty of careful attention paid to refining statistical design, but basically this stays safely within the boundaries of current methods and beliefs, and the need for research continuity. It may be very understandable, because one can't just quickly uproot everything or order up deep new insights. But it may be viewed as abuse of public trust as well as of the science itself.
The BBC Radio 4 program called More Or Less keeps a watchful eye on sociopolitical and scientific statistical claims, revealing what is really known (or not) about them. Here is a recent installment on the efficacy (or believability, or neither) of dietary surveys. And here is a FiveThirtyEight link to what was the basis of the podcast.
The promotion of statistical survey studies to assert fundamental discovery has been referred to as 'promissory science'. We are barraged daily with promises that if we just invest in this or that Big Data study, we will put an end to all human ills. It's a strategy, a tactic, and at least the top investigators are very well aware of it. Big long-term studies are a way to secure reliable funding and to defer delivering on promises into the vague future. The funding agencies, wanting to seem prudent and responsible to taxpayers with their resources, demand some 'societal impact' section on grant applications. But there is in fact little if any accountability in this regard, so one can say they are essentially bureaucratic window-dressing exercises.
Promissory science is an old game, practiced since time immemorial by preachers. It boils down to promising future bliss if you'll just pay up now. We needn't be (totally) cynical about this. When we set up a system that depends on public decisions about resources, we will get what we've got. But having said that, let's take a look at what is a growing recognition of the problem, and some suggestions as to how to fix it--and whether even these are really the Emperor of promissory science dressed in less gaudy clothing.
A growing at least partial awareness
The problem of results that are announced by the media, journals, universities, and so on but that don't deliver the advertised promises is complex but widespread, in part because research has become so costly, that some warning sirens are sounding when it becomes clear that the promised goods are not being delivered.
One widely known issue is the lack of reporting of negative results, or their burial in minor journals. Drug-testing research is notorious for this under-reporting. It's too bad because a negative result on a well-designed test is legitimately valuable and informative. A concern, besides corporate secretiveness, is that if the cost is high, taxpayers or share-holders may tire of funding yet more negative studies. Among other efforts, including by NIH, there is a formal attempt called AllTrials to rectify the under-reporting of drug trials, and this does seem at least to be thriving and growing if incomplete and not enforceable. But this non-reporting problem has been written about so much that we won't deal with it here.
Instead, there is a different sort of problem. The American Statistical Association has recently noted an important issue, which is the use and (often) misuse of p-values to support claims of identified causation (we've written several posts in the past about these issues; search on 'p-value' if you're interested, and the post by Jim Wood is especially pertinent). FiveThirtyEight has a good discussion of the p-value statement.
The usual interpretation is that p represents the probability that if there is in fact no causation by the test variable, that its apparent effect arose just by chance. So if the observed p in a study is less than some arbitrary cutoff, such as 0.05, it means essentially that if no causation were involved the chance you'd see this association anyway is no greater than 5%; that is, there is some evidence for a causal connection.
Trashing p-values is becoming a new cottage industry! Now JAMA is on the bandwagon, with an article that shows in a survey of biomedical literature from the past 25 years, including well over a million papers, a far disproportionate and increasing number of studies reported statistical significant results. Here is the study on the JAMA web page, though it is not public domain yet.
Besides the apparent reporting bias, the JAMA study found that those papers generally failed to provide adequate fleshing out of that result. Where are all the negative studies that statistical principles might expect to be found? We don't see them, especially in the 'major' journals, as has been noted many times in recent years. Just as importantly, authors often did not report confidence intervals or other measures of the degree of 'convincingness' that might illuminate the p-value. In a sense that means authors didn't say what range of effects is consistent with the data. They report a non-random effect, but often didn't give the effect size, that is, say how large the effect was even assuming that effect was unusual enough to support a causal explanation. So, for example, a statistically significant increase of risk from 1% to 1.01% is trivial, even if one could accept all the assumptions of the sampling and analysis.
Another vocal critic of what's afoot is John Ionnides; in a recent article he levels both barrels against the misuse and mis- or over-representation of statistical results in biomedical sciences, including meta-analysis (the pooling of many diverse small studies into a single large analysis to gain sufficient statistical power to detect effects and test for their consistency). This paper is a rant, but a well-deserved one, about how 'evidence-based' medicine has been 'hijacked' as he puts it. The same must be said of 'precision genomic' or 'personalized' medicine, or 'Big Data', and other sorts of imitative sloganeering going on from many quarters who obviously see this sort of promissory science as what you have to do to get major funding. We have set ourselves a professional trap, and it's hard to escape. For example, the same author has been leading the charge against misrepresentative statistics for many years, and he and others have shown that the 'major' journals have in a sense the least reliable results in terms of their replicability. But he's been raising these points in the same journals that he shows are culpable of the problem, rather than boycotting those journals. We're in a trap!
These critiques of current statistical practice are the points getting most of the ink and e-ink. There may be a lot of cover-ups of known issues, and even hypocrisy, in all of this, and perhaps more open or understandable tacit avoidance. The industry (e.g., drug, statistics, and research equipment) has a vested interest in keeping the motor running. Authors need to keep their careers on track. And, in the fairest and non-political sense, the problems are severe.
But while these issues are real and must be openly addressed, I think the problems are much deeper. In a nutshell, I think they relate to the nature of mathematics relative to the real world, and the nature and importance of theory in science. We'll discuss this tomorrow.
You can't drive without a license, but anyone with R or SAS can be a push-button scientist. Anybody with a keyboard and some survey generating software can monkey around with asking people a bunch of questions and then 'analyze' the results. You can construct a complex, long, intricate, jargon-dense, expansive survey. You then choose who to subject to the survey--your 'sample'. You can grace the results with the term 'data', implying true representation of the world, and be off and running. Sample and survey designers may be intelligent, skilled, well-trained in survey design, and of wholly noble intent. There's only one little problem: if the empirical fit is poor, much of what you do will be non-sense (and some of it nonsense).
Population sciences, including biomedical, evolutionary, social and political fields are experiencing an increasingly widely recognized crisis of credibility. The fault is not in the statistical methods on which these fields heavily depend, but in the degree of fit (or not) to the assumptions--with the emphasis these days on the 'or not', and an often dismissal of the underlying issues in favor of a patina of technical, formalized results. Every capable statistician knows this, but of course might be out of business if openly paying it enough attention. And many statisticians may be rather disinterested or too foggy in the philosophy of science to understand what goes beyond the methodological technicalities. Jobs and journals depend on not being too self-critical. And therein lie rather serious problems.
Promissory science
There is the problem of the problems--the problems we want to solve, such as in understanding the cause of disease so that we can do something about it. When causal factors fit the assumptions, statistical or survey study methods work very well. But when causation is far from fitting the assumptions, the impulse of the professional community seems mainly to increase the size, scale, cost, and duration of studies, rather than to slow down and rethink the question itself. There may be plenty of careful attention paid to refining statistical design, but basically this stays safely within the boundaries of current methods and beliefs, and the need for research continuity. It may be very understandable, because one can't just quickly uproot everything or order up deep new insights. But it may be viewed as abuse of public trust as well as of the science itself.
The BBC Radio 4 program called More Or Less keeps a watchful eye on sociopolitical and scientific statistical claims, revealing what is really known (or not) about them. Here is a recent installment on the efficacy (or believability, or neither) of dietary surveys. And here is a FiveThirtyEight link to what was the basis of the podcast.
The promotion of statistical survey studies to assert fundamental discovery has been referred to as 'promissory science'. We are barraged daily with promises that if we just invest in this or that Big Data study, we will put an end to all human ills. It's a strategy, a tactic, and at least the top investigators are very well aware of it. Big long-term studies are a way to secure reliable funding and to defer delivering on promises into the vague future. The funding agencies, wanting to seem prudent and responsible to taxpayers with their resources, demand some 'societal impact' section on grant applications. But there is in fact little if any accountability in this regard, so one can say they are essentially bureaucratic window-dressing exercises.
Promissory science is an old game, practiced since time immemorial by preachers. It boils down to promising future bliss if you'll just pay up now. We needn't be (totally) cynical about this. When we set up a system that depends on public decisions about resources, we will get what we've got. But having said that, let's take a look at what is a growing recognition of the problem, and some suggestions as to how to fix it--and whether even these are really the Emperor of promissory science dressed in less gaudy clothing.
A growing at least partial awareness
The problem of results that are announced by the media, journals, universities, and so on but that don't deliver the advertised promises is complex but widespread, in part because research has become so costly, that some warning sirens are sounding when it becomes clear that the promised goods are not being delivered.
One widely known issue is the lack of reporting of negative results, or their burial in minor journals. Drug-testing research is notorious for this under-reporting. It's too bad because a negative result on a well-designed test is legitimately valuable and informative. A concern, besides corporate secretiveness, is that if the cost is high, taxpayers or share-holders may tire of funding yet more negative studies. Among other efforts, including by NIH, there is a formal attempt called AllTrials to rectify the under-reporting of drug trials, and this does seem at least to be thriving and growing if incomplete and not enforceable. But this non-reporting problem has been written about so much that we won't deal with it here.
Instead, there is a different sort of problem. The American Statistical Association has recently noted an important issue, which is the use and (often) misuse of p-values to support claims of identified causation (we've written several posts in the past about these issues; search on 'p-value' if you're interested, and the post by Jim Wood is especially pertinent). FiveThirtyEight has a good discussion of the p-value statement.
The usual interpretation is that p represents the probability that if there is in fact no causation by the test variable, that its apparent effect arose just by chance. So if the observed p in a study is less than some arbitrary cutoff, such as 0.05, it means essentially that if no causation were involved the chance you'd see this association anyway is no greater than 5%; that is, there is some evidence for a causal connection.
Trashing p-values is becoming a new cottage industry! Now JAMA is on the bandwagon, with an article that shows in a survey of biomedical literature from the past 25 years, including well over a million papers, a far disproportionate and increasing number of studies reported statistical significant results. Here is the study on the JAMA web page, though it is not public domain yet.
Besides the apparent reporting bias, the JAMA study found that those papers generally failed to provide adequate fleshing out of that result. Where are all the negative studies that statistical principles might expect to be found? We don't see them, especially in the 'major' journals, as has been noted many times in recent years. Just as importantly, authors often did not report confidence intervals or other measures of the degree of 'convincingness' that might illuminate the p-value. In a sense that means authors didn't say what range of effects is consistent with the data. They report a non-random effect, but often didn't give the effect size, that is, say how large the effect was even assuming that effect was unusual enough to support a causal explanation. So, for example, a statistically significant increase of risk from 1% to 1.01% is trivial, even if one could accept all the assumptions of the sampling and analysis.
Another vocal critic of what's afoot is John Ionnides; in a recent article he levels both barrels against the misuse and mis- or over-representation of statistical results in biomedical sciences, including meta-analysis (the pooling of many diverse small studies into a single large analysis to gain sufficient statistical power to detect effects and test for their consistency). This paper is a rant, but a well-deserved one, about how 'evidence-based' medicine has been 'hijacked' as he puts it. The same must be said of 'precision genomic' or 'personalized' medicine, or 'Big Data', and other sorts of imitative sloganeering going on from many quarters who obviously see this sort of promissory science as what you have to do to get major funding. We have set ourselves a professional trap, and it's hard to escape. For example, the same author has been leading the charge against misrepresentative statistics for many years, and he and others have shown that the 'major' journals have in a sense the least reliable results in terms of their replicability. But he's been raising these points in the same journals that he shows are culpable of the problem, rather than boycotting those journals. We're in a trap!
These critiques of current statistical practice are the points getting most of the ink and e-ink. There may be a lot of cover-ups of known issues, and even hypocrisy, in all of this, and perhaps more open or understandable tacit avoidance. The industry (e.g., drug, statistics, and research equipment) has a vested interest in keeping the motor running. Authors need to keep their careers on track. And, in the fairest and non-political sense, the problems are severe.
But while these issues are real and must be openly addressed, I think the problems are much deeper. In a nutshell, I think they relate to the nature of mathematics relative to the real world, and the nature and importance of theory in science. We'll discuss this tomorrow.
Kadın , İnsan , hayat
Kadına şiddet , İnsana Şiddet , hayvana şiddet her yerde şiddet
Bir kaç gündür canım çok sıkkın , nasıl bir dünyada yaşıyoruz anlayamıyorum .Vahşi hayvanları bile geçtik onlar hiç değilse karınlarını doyurmak ve yaşama dürtüsüyle avlanıyorlar ya biz'
Son yaşadığımız üzücü olay bize ders olur mu?
Hiç sanmıyorum ve çok endişeleniyorum , çok ürküyorum , nereye gidiyor böyle ne olacak sonunda herkes bir birini parçalayıp zafer dansları mı yapacak etrafında!
En küçük topluluk olan aileler bile o kadar kolay parçalanıyor ki bu aralar , kim suçlu kim haklı polemiğine girmeye kalksak ne ömrümüz yeter ne de sabrımız.
Aile olarak bile bütünlüğü koruyamıyorsak ,Vatan olarak nasıl koruyacağız?
Sayın Erkek sana soruyorum evde karına dışarıda kız arkadaşına gücün yeter kolunu büker tokat atarsın , ne oldu gücün yetmedi mi teroriste o mu sana attı tekmeyi.
Amazon kadınlarına çok hak veriyorum keşke yine bir amazon Ülkesi olsa en başta ben giderdim. bu kızgınlığımın sebebi yine şiddet!
Bu gün işte çalışırken bir arkadaşımın gözünün üstü dikkatimi çekti , göz kalemi bulaşmış sanıp gayri ihtiyari elimle silmek istedim , geri çekti kendini , eşi dövmüş!
Hiç yolda görsen bu adama ne kadar efendi dersin .
sorun ne dedim?
Adamın annesi hastaneye yatmış bunlar tartışmış ana , oğul sonra oğul hastaneye gitmiş elini vermemiş annesi tabii eve de yansımış olay. zaten gelin kaynana hiç anlaşamıyor kayınvalide hep gelinin dedikodusunda. Aynı apartmanda oturuyorlar ,üstelik arkadaşım çalışıyor yinede hep şikayet eşin bizi sevmiyor diyormuş. sevmek zorunda mı ki saygı gösteriyorsa derdin ne?
Neyse bunlar yatağa yatmış adam demiş ben arabayı sattım
bin lira zararına demiş.
Neden sattın diye tartışmışlar ben olsam bana sormadan nasıl satarsın diyede kızardım kafasına göre iş yapamaz. arkadaşımın tek dediği annen gibisin kelimesiymiş elinin tersiyle yumrukla dövmüş , bir gün işe gelmedi hiçte aklıma gelmedi çünkü adam böyle biri değil ama bir defa affederse yine yapacak affetme dedim. Boşayacağım dedi .
Ya boşama öyle aile yıkmak kolay değil af etme sadece af etme bu konuyu ailene anlat , bilsinler senin değil onun utanması lazım, ben çok karakterli ve güçlü görüyordum malesef korkak ve iradesiz bir erkekmiş onlar barışacak her şey güzel olacak ama bu adam artık gözümde iki paralık.
İleride bizde böylemi yapacağız çocuklarımızın ailelerini parçalamak için böyle uğraşacakmıyız sonrada ellerine düşüp bakmalarını mı bekleyeceğiz.
Siz olsanız arkadaşınıza ne öneriridiniz?
Obesity and diabetes: Actual epigenetics or just IVF?
This press release that appeared in my newsfeed titled "You are what your parents ate!" caught my eye because I'm a new mom of a new human and also because I study and teach human evolution.
So I clicked on it.
And after that title primed me to think about me!, the photo further encouraged my assumption that this is really all about humans.
But it's about mice. Yes, evolution, I know, I know. We share common ancestry with mice which is why they can be good experimental models for understanding our own biology. But we have been evolving separately from mice for a combined total of over 100 million years. Evolution means we're similar, yes, but evolution also means we're different.
Bah. It's still fascinating, mice or men, womice or women! So I kept reading and learned how new mice made with IVF--that is, made of eggs and sperm from lab-induced obese and diabetic mouse parents, but born of healthy moms--inherited the metabolic troubles of their biological parents. And by inherited, we're not talking genetically, because these phenotypes are lab-induced. We're talking epigenetically. So the eggs and sperm did it, but not the genomes they carry!
This isn't so surprising if you've been following the burgeoning field of epigenetics, but it's hard to look away. This fits with how we see secular increases in human obesity and adult-onset diabetes--it can't be genomic evolution, it must be epigenetic evolution, whatever that means!
As the press release says...
Except when you remember a two-year-old piece by Bethany Brookshire (because you use it to teach a course on sex and reproduction) which explained something that suggests we may have a major experimental problem with the study above.
In IVF, the sperm gets isolated (or "washed") from the semen.
You know what happens, to mice in particular, when there's no semen? Obesity and other symptoms of metabolic syndrome! There are placental differences too. This was published in PNAS.
So I went back to look at the original paper that the press release with the donut lady was about. I wanted to see if they are aware of this potential problem with IVF and whether it explains their findings, rather than the trendy concept of epigenetics...
So even though they titled it "Epigenetic germline inheritance of diet-induced obesity and insulin resistance," I wanted to see if they at least accounted for this trouble with semen, like how it's probably important, how its absence may bring about the same phenotypes they're tracking, and how IVF doesn't use semen.
But I don't have access to Nature Genetics.
Who has access to Nature Genetics, can check out the paper, and wants to write the ending of this blog post?
Step right up! Post your work in the comments (or email me holly_dunsworth@uri.edu, and please include a pdf of the paper so I can see too) and I'll paste it right here.
Update 12:19 pm
Two very good comments below are helpful. Please read those.
I'll add that I now have the pdf of the paper (but not the Supplemental portion where all the methods live and other important information resides). This quote from the second paragraph implies they do not agree with the finding of (or have forgotten about) the phenotypic variation apparently caused by sperm washed of their seminal fluid:
Completely unrelated and inescapable... I'm a little curious about how the authors decided to visualize their data like this:
So I clicked on it.
And after that title primed me to think about me!, the photo further encouraged my assumption that this is really all about humans.
 |
| "You are what your parents ate!" |
But it's about mice. Yes, evolution, I know, I know. We share common ancestry with mice which is why they can be good experimental models for understanding our own biology. But we have been evolving separately from mice for a combined total of over 100 million years. Evolution means we're similar, yes, but evolution also means we're different.
Bah. It's still fascinating, mice or men, womice or women! So I kept reading and learned how new mice made with IVF--that is, made of eggs and sperm from lab-induced obese and diabetic mouse parents, but born of healthy moms--inherited the metabolic troubles of their biological parents. And by inherited, we're not talking genetically, because these phenotypes are lab-induced. We're talking epigenetically. So the eggs and sperm did it, but not the genomes they carry!
This isn't so surprising if you've been following the burgeoning field of epigenetics, but it's hard to look away. This fits with how we see secular increases in human obesity and adult-onset diabetes--it can't be genomic evolution, it must be epigenetic evolution, whatever that means!
As the press release says...
"From the perspective of basic research, this study is so important because it proves for the first time that an acquired metabolic disorder can be passed on epigenetically to the offspring via oocytes and sperm- similar to the ideas of Lamarck and Darwin," said Professor ...Whole new ways of thinking are so exciting.
Except when you remember a two-year-old piece by Bethany Brookshire (because you use it to teach a course on sex and reproduction) which explained something that suggests we may have a major experimental problem with the study above.
In IVF, the sperm gets isolated (or "washed") from the semen.
You know what happens, to mice in particular, when there's no semen? Obesity and other symptoms of metabolic syndrome! There are placental differences too. This was published in PNAS.
 |
| "Offspring of male mice without seminal fluid had bigger placentas (top right) and increased body fat (bottom right) compared with offspring of normal male mice (left images)" from The fluid part of semen plays a seminal role by Bethany Brookshire. |
So I went back to look at the original paper that the press release with the donut lady was about. I wanted to see if they are aware of this potential problem with IVF and whether it explains their findings, rather than the trendy concept of epigenetics...
So even though they titled it "Epigenetic germline inheritance of diet-induced obesity and insulin resistance," I wanted to see if they at least accounted for this trouble with semen, like how it's probably important, how its absence may bring about the same phenotypes they're tracking, and how IVF doesn't use semen.
But I don't have access to Nature Genetics.
Who has access to Nature Genetics, can check out the paper, and wants to write the ending of this blog post?
Step right up! Post your work in the comments (or email me holly_dunsworth@uri.edu, and please include a pdf of the paper so I can see too) and I'll paste it right here.
Update 12:19 pm
Two very good comments below are helpful. Please read those.
I'll add that I now have the pdf of the paper (but not the Supplemental portion where all the methods live and other important information resides). This quote from the second paragraph implies they do not agree with the finding of (or have forgotten about) the phenotypic variation apparently caused by sperm washed of their seminal fluid:
"The use of IVF enabled us to ensure that any inherited phenotype was exclusively transmitted via gametes."As the second commenter (Anonymous) pointed out below, there does not appear to be a comparison of development or behavior between any of the IVF mice and mice made by mouse sex. So there is no way to tell whether their IVF mice exhibit the same metabolic changes that the semen/semenless study found. Therefore, it is neither possible to work the semen issue into the explanation nor to rule out its effects. Seems like a missed opportunity.
Completely unrelated and inescapable... I'm a little curious about how the authors decided to visualize their data like this:
İç Ses - 23
Bir mesaj, program yükleniyor arkadaşlar bir kaç dakika kaldı. Onun üzerine instagrama yüklenen biz bir iş yapıyoruz izleyin diye de heyecanla bekliyoruz demeye çalıştığım içinde kadın kahkahamız olan bir kamera arkası videosu. Sadece birkaç dakika sonra Ankara'da patlama oldu yayını gizliye aldık denilen bir bilgi mesajı. Uyuşma etkisi ile geçen bir akşam. Uyuşuk bir sabah, anneannemin uyanışı, çay koyuşum, televizyondaki eğlence programlarını yayınlamamışlar iyi bari diye içimden geçirişim, kahvaltıda yemek üzere haşladığım yumurta, okul için yaptığım tost, yürüdüğüm yol, telefonuma düşen mesajlar çalışarak ve birbirimize sarılarak aşmak zorundayız diyor hocam, öfkem burnumda okul yolundayım. Anlık insanı kaygılara düşüyorum. Üşüyorum, çamura basıp hayıflanıyorum sonra utanıyorum. Ahlaksızlığımıza yanıyorum. Okula gittiğimde patlamak üzereyim öfkem acımdan ağır basıyor. Başka bir hocam geliyor sınıfa, acısı onun da gözünün ucunda konuşuyoruz uzun uzun. Birbirinize yaklaşın diyor en sonunda, devam etmek zorundayız. Bilim ve sanat diyor. Devam ediyoruz. İyi geldik birbirimize hiç temas etmeden elimizi tuttuk birbirimizin. Biz devam ederken mail kutuma bağlantı kurmaya çalıştığım bir yazarın iletişim numarası düşüyor. Mutlu oluyorum. Arıyorum karşımda kibar bir adam kafam biraz bulanık bugün diyor , anlıyorum onu, kapatırken birbirimize baş sağlığı diliyoruz. Birbirini hiç tanımadan aynı yasta birleşmiş koca cenaze evinin insanlarıyız çünkü artık. Eve dönmeliyim annem ilaç almaya gidecek anneannem yalnız kalamaz. Toplu taşımada patlar mıyım ben de? Biz kendimizi ve o çocukları her şeye rağmen üretmeye ikna edebilmek için çalışmıştık, onlar izlesin, hayal etsin ve hep beraber daha iyiye dair umut edebilelim diye. O gençler,insanlar bir gelecek hayal ediyorlardı. O insanlar , biz, ölüm, otobüs, bomba ... Ne olacak, nasıl olacak hayat devam edecek tamam da biz ruhumuz çürümeden nasıl nefes alacağız. Bütün kelimelerimiz bittiğinde mi kazanmış olacaklar ? Ne zaman yetmiş olacak tam olarak ? Biz ne zaman yaşayabileceğiz ? Korkmadan, utanmadan, insanca ... Ne zaman ?
Nasihat
Dost
Bir dost'un varsa çok şeyin vardır ,
Ekmeğin vardır, suyun vardır,
Akan çatına kabın vardır,
Ağlarken güldürenin vardır,
En sinirli anında sana , dur sakin ol diyenin vardır ,
Seni senden çok savunanın vardır ,
Dost acı söyleyen değil acıyı, acıtmadan söyleyendir
Bir dostun varsa kısacası
Kalbinin kararmış köşelerini itina ile kazıyıp sıvayıp , bembeyaz yapacak biri vardır...
Murmurations and you
I have a doctorate in Public Health which means that, unlike a 'real doctor', I was trained to think in terms of the health of populations, not of specific individuals. Public Health of course, when done appropriately, can have an enormous impact on the health of individuals, but in a very real way that's a side effect of gathering group information and instituting measures meant to affect a group. Clean water, fluoridated water, vaccinations, window screens, anti-smoking campaigns, and so much more are all public health measures targeting whole populations, without regard for the specific cavities or cases of cholera or lung cancer that the measure will actually prevent. This is because, of course, smoking doesn't make every smoker sick, just enough of them that aiming to convince whole populations not to smoke can have a large enough difference on population health that it's worth the cost and effort.
You've probably seen those murmuration videos showing enormous flocks of birds flying as if they were one; undulating, turning, responding as though they have a collective mind. Here's one is of a flock of starlings being hunted by a peregrine falcon one evening in Rome. The starlings fly so unpredictably that, at least this time, the falcon is unable to catch a meal.
Source: BBC One
According to the Cornell Lab of Ornithology, murmurations almost always arise in response to the detection of a predator; a falcon or a hawk that has come for its dinner, as the starlings in Rome. So, a bird or birds detect the predator and sound the alarm, which triggers the whole flock to take off. But, how do they stay together? Who decides where they're going next, and how does the rest of the flock get the message?
Young et al. report, in a 2013 paper in PLOS Computational Biology, that once in flight each bird is noticing and responding to the behavior only of its seven nearest neighbors. The murmuration, the movement of the group, then, is due to local responses that create the waves of motion that can be seen in the evening sky. There is no single leader, just many, many local responses happening almost simultaneously.
The same kinds of dynamics explain the movements of schools of fish as well. They work to some extent, but fish are routinely attacked by sharks, which can scoop up multiple individuals at a time, and surely sometimes birds of prey manage to snap up a luckless bird among the thousands or millions in a flock. But, most of the fish or the birds do get away, so it's a winning strategy for the group. Public Health in action.
Well-known, very prolific British epidemiologist George Davey Smith was interviewed on the BBC Radio 4 program The Life Scientific not long ago. He's a medical doctor with a degree in Public Health as well, so he's been trained to think in terms of both the population and the individual. He is currently interested in what genes can tell us about environmental influences on health. One of his contributions to this question is the analytical tool called Mendelian Randomization, which aims to tease out environmental triggers of a trait given a particular genetic risk factor. That is, the idea is to divide a study sample into individuals with and without a particular genetic variant, to determine whether their history of exposure to an apparent risk factor might be responsible for the disease. In this instance, the gene isn't modifiable, but exposure might be.
In the interview, Davey Smith said that his primary interest is in population health, and that if a Public Health measure can reduce incidence of disease, he's happy. So, if everyone in a population is on statins, say, and that reduces heart disease and stroke without major side effects, he would consider that a successful Public Health measure. Even if it's impossible to know just who's stroke or heart attack was prevented. Success of Public Health can only be evaluated on the population, not the individual level.
So much for personalized, predictive medicine. That's fine, my training is in Public Health, too, so I'm ok with that. Except that Davey Smith is also a fan of large, longitudinal studies maintained in perpetuity because, as he said, they have yielded more results at lower cost than most any other kind of epidemiological study.
But there are problems with such studies, and if the idea is to identify modifiable environmental risk factors, a major problem is that these studies are always retrospective. And, as we've written here so often, future environments are not predictable in principle. Presumably the aim of these large studies is to use Big Data to determine which Public Health measures are required to reduce risk of which diseases, and if that is done -- so that large segments of the population are put on statins or change from saturated to unsaturated fats or start to exercise or quit smoking -- this changes environmental exposures, and thus the suite of diseases that people are then at risk of.
So, Public Health has to always be playing catch up. Controlling infectious diseases can be said to have been a cause of the increase in cancer and obesity and heart disease and stroke, by increasing the number of people who avoided infectious disease to live to be at risk of these later diseases. So, in that sense, putting whole populations on statins is going to cause the next wave of diseases that will kill most of us, even if we don't yet know what these diseases will be. Maybe even infectious diseases we currently know nothing about.
Even though, after putting their favored Public Health measure into effect, all the starlings outwitted the falcon that particular night in Rome, they're all eventually going to die of something.
You've probably seen those murmuration videos showing enormous flocks of birds flying as if they were one; undulating, turning, responding as though they have a collective mind. Here's one is of a flock of starlings being hunted by a peregrine falcon one evening in Rome. The starlings fly so unpredictably that, at least this time, the falcon is unable to catch a meal.
Source: BBC One
According to the Cornell Lab of Ornithology, murmurations almost always arise in response to the detection of a predator; a falcon or a hawk that has come for its dinner, as the starlings in Rome. So, a bird or birds detect the predator and sound the alarm, which triggers the whole flock to take off. But, how do they stay together? Who decides where they're going next, and how does the rest of the flock get the message?
Young et al. report, in a 2013 paper in PLOS Computational Biology, that once in flight each bird is noticing and responding to the behavior only of its seven nearest neighbors. The murmuration, the movement of the group, then, is due to local responses that create the waves of motion that can be seen in the evening sky. There is no single leader, just many, many local responses happening almost simultaneously.
The same kinds of dynamics explain the movements of schools of fish as well. They work to some extent, but fish are routinely attacked by sharks, which can scoop up multiple individuals at a time, and surely sometimes birds of prey manage to snap up a luckless bird among the thousands or millions in a flock. But, most of the fish or the birds do get away, so it's a winning strategy for the group. Public Health in action.
Well-known, very prolific British epidemiologist George Davey Smith was interviewed on the BBC Radio 4 program The Life Scientific not long ago. He's a medical doctor with a degree in Public Health as well, so he's been trained to think in terms of both the population and the individual. He is currently interested in what genes can tell us about environmental influences on health. One of his contributions to this question is the analytical tool called Mendelian Randomization, which aims to tease out environmental triggers of a trait given a particular genetic risk factor. That is, the idea is to divide a study sample into individuals with and without a particular genetic variant, to determine whether their history of exposure to an apparent risk factor might be responsible for the disease. In this instance, the gene isn't modifiable, but exposure might be.
In the interview, Davey Smith said that his primary interest is in population health, and that if a Public Health measure can reduce incidence of disease, he's happy. So, if everyone in a population is on statins, say, and that reduces heart disease and stroke without major side effects, he would consider that a successful Public Health measure. Even if it's impossible to know just who's stroke or heart attack was prevented. Success of Public Health can only be evaluated on the population, not the individual level.
So much for personalized, predictive medicine. That's fine, my training is in Public Health, too, so I'm ok with that. Except that Davey Smith is also a fan of large, longitudinal studies maintained in perpetuity because, as he said, they have yielded more results at lower cost than most any other kind of epidemiological study.
But there are problems with such studies, and if the idea is to identify modifiable environmental risk factors, a major problem is that these studies are always retrospective. And, as we've written here so often, future environments are not predictable in principle. Presumably the aim of these large studies is to use Big Data to determine which Public Health measures are required to reduce risk of which diseases, and if that is done -- so that large segments of the population are put on statins or change from saturated to unsaturated fats or start to exercise or quit smoking -- this changes environmental exposures, and thus the suite of diseases that people are then at risk of.
So, Public Health has to always be playing catch up. Controlling infectious diseases can be said to have been a cause of the increase in cancer and obesity and heart disease and stroke, by increasing the number of people who avoided infectious disease to live to be at risk of these later diseases. So, in that sense, putting whole populations on statins is going to cause the next wave of diseases that will kill most of us, even if we don't yet know what these diseases will be. Maybe even infectious diseases we currently know nothing about.
Even though, after putting their favored Public Health measure into effect, all the starlings outwitted the falcon that particular night in Rome, they're all eventually going to die of something.
Yağmur
yağmurlu bir gün çık karşıma sımsıkı sarayım seni
usulca dokun omzuma, doya doya koklayayım seni.
yoldan sessizce geçen bir kedi ,şahit olsun aşkımıza.
rüzgar usulca ,seni ne kadar sevdiğimi fısıldasın kulağına,
doya doya göreyim seni
yağmur sel olup aksın yollardan , aktığı yerlerde izler bıraksın,
sevdan nasıl aktıysa yüreğime , nasıl izler bıraktıysa derinlerde
yağmurlu bir gün çık karşıma,saçların yapışsın alnına
kirpiklerinden süzülsün ,gül yüzüne,
ıslandıkça kokun sinsin üstüme,sen soğuktan titre,
ben aşkından...
ben aşkından...
sımsıkı sarıl bana hiç ayrılmayacak gibi
öyle bir bak ki gözlerime,
gözlerimi kapadığımda silüetin gelsin karşıma
gözlerimi kapadığımda silüetin gelsin karşıma
yağmurun sesi karışsın kalp atışıma,
hangi daha hızlı hissetmeyeyim öyle gül ki gözlerime
alıp götürsün beni buralardan ıssız vadilere.
yağmurlu bir gün gir yüreğime en kuytu köşelerine
öyle bir söz söyle ki bana,
seni neden bu kadar çok sevdiğimi
haykırayım kendime ...
When evolutionary-minded medicine gets it (possibly) wrong about childbirth interventions
No one is saying that medicine isn't brilliant and hasn't saved lives. But it does intervene more than necessary when it comes to pregnancy and childbirth.
Part of that unnecessary intervention is driven by lack of experience. Part is an economically-driven disrespect for time. (Give childbirth some motherlovin' time.) Another part, related very much to experience, is how difficult it is to decide when intervention is and isn't necessary, especially when things are heating up. But another part of the trouble actually lies in the evolutionary perspective. Unfortunately it's not all rainbows and unicorns when M.D.s embrace evolution. Instead, evolutionary thinking is biasing some medical professionals into believing that, for example, birth by surgical caesarean is an "evolutionary imperative."
Here's one recent example in The American Journal of Obstetrics & Gynecology of how the evolutionary perspective is (mis)guiding arguments for increased medical intervention in childbirth.
It's a fairly straight-forward study of over 22,000 birth records at a hospital in Jerusalem. The authors ask whether birth weight (BW) or head circumference (HC) is more of a driver of childbirth interventions (instrumental delivery and unplanned caesareans) than the other. Of course, the focus is on the biggest babies with the biggest heads causing all the trouble, so the authors narrow the data down to the 95th percentile for both. Presumably they're asking this question about BW and HC because both can be estimated with prenatal screening. So there's the hope of improving delivery outcomes here. And, of course, the reason they ask whether head size or body mass is more of a problem is because of evolution. They anticipate that they'll discover that heads are a bigger problem than bodies because of the well-known "obstetrical dilemma" (OD) hypothesis in anthropology.
OD thinking goes like this: Big heads and small birth canals are adaptive for our species' cognition and locomotion, respectively, but the two traits cause a problem at birth, which is not only difficult but results in our species' peculiar brand of useless babies. (But see and see.)
So, since we're on the OD train, it's no surprise when we read how the authors demonstrate and, thus, conclude that indeed HC (head circumference) is more strongly associated with childbirth interventions than BW (birth weight), at least when we're up in the 95th percentile of BW and HC. Okay.
They use this finding to advocate for prenatal estimation of head size to prepare for any difficulties a mother and her fetus may be facing soon. Okay.
Sounds good. Sounds really good if you support healthy moms and babies. But it also sounds really good if you already see these risks to childbirth through the lens of the "obstetrical dilemma" with that OD thinking helping you to support "the evolutionary imperative" of the c-section. Okay.
Too many "Okays" you're thinking? You're right. There's a catch.
When you dig into the paper you see that "large HC" heads are usually about an inch (~ 2.5 cm) greater in circumference than "normal HC" ones. (Nevermind that we chopped up a continuum of quantitative variation to put heads in arbitrary categories for statistical analysis.) And when you calculate the head diameter based on the head circumference, there is less than 1 cm difference between "large" and "normal" neonatal heads in diameter. That doesn't seem like a whole lot considering how women's bony pelvic dimensions can vary more than that. Still, these data suggest that the difference between a relatively low risk of having a c-section and a relatively high risk of having a c-section amounts to less than a centimeter in fetal head diameter. And maybe it does. Nobody's saying that big heads aren't a major problem sometimes! But maybe there's something else to consider that the paper absolutely didn't.
Neonatal heads get squeezed and molded into interesting shapes in the birth canal.
The data say that normal HC babies get born vaginally more often than large HC ones. But this is based on the head measures of babies who are already born! If we're pitting head circumference (HC) of babies plucked from the uterus against the HC of babies who've been through hello! then of course the vaginally delivered ones could have smaller HCs.
C-sected babies tend to have rounder heads than the ones squeezed by the birth canal. It's impossible to know but I'm fairly confident about this, at least for a subsample of a population: Birth the same baby from the same mother both ways, vaginally and surgically, and its head after c-section will have a larger HC than its squeezed conehead will after natural birth.
When we're talking about roughly 2.5 cm difference in circumference or less than 1 cm difference in diameter, then I'd say it's possible that neonatal cranial plasticity is mucking up these data; we're sending c-sected babies over into the "large HC" part of the story just because they were c-sected in the first place. So without accounting for this phenomenon, the claim that large head circumference is more of a cause of birth intervention, of unplanned c-sections, than large body mass isn't as believable.
If these thoughts about neonatal cranial molding are worthwhile, then here we have a seemingly useful and very high-profile professional study, grounded in the popular but deeply flawed obstetrical dilemma hypothesis, that is arguing for medical intervention in childbirth based solely on the difference in head size measures induced by those very medical interventions.
The circle of life!
Part of that unnecessary intervention is driven by lack of experience. Part is an economically-driven disrespect for time. (Give childbirth some motherlovin' time.) Another part, related very much to experience, is how difficult it is to decide when intervention is and isn't necessary, especially when things are heating up. But another part of the trouble actually lies in the evolutionary perspective. Unfortunately it's not all rainbows and unicorns when M.D.s embrace evolution. Instead, evolutionary thinking is biasing some medical professionals into believing that, for example, birth by surgical caesarean is an "evolutionary imperative."
Here's one recent example in The American Journal of Obstetrics & Gynecology of how the evolutionary perspective is (mis)guiding arguments for increased medical intervention in childbirth.
 |
| link to paper |
OD thinking goes like this: Big heads and small birth canals are adaptive for our species' cognition and locomotion, respectively, but the two traits cause a problem at birth, which is not only difficult but results in our species' peculiar brand of useless babies. (But see and see.)
So, since we're on the OD train, it's no surprise when we read how the authors demonstrate and, thus, conclude that indeed HC (head circumference) is more strongly associated with childbirth interventions than BW (birth weight), at least when we're up in the 95th percentile of BW and HC. Okay.
They use this finding to advocate for prenatal estimation of head size to prepare for any difficulties a mother and her fetus may be facing soon. Okay.
Sounds good. Sounds really good if you support healthy moms and babies. But it also sounds really good if you already see these risks to childbirth through the lens of the "obstetrical dilemma" with that OD thinking helping you to support "the evolutionary imperative" of the c-section. Okay.
Too many "Okays" you're thinking? You're right. There's a catch.
When you dig into the paper you see that "large HC" heads are usually about an inch (~ 2.5 cm) greater in circumference than "normal HC" ones. (Nevermind that we chopped up a continuum of quantitative variation to put heads in arbitrary categories for statistical analysis.) And when you calculate the head diameter based on the head circumference, there is less than 1 cm difference between "large" and "normal" neonatal heads in diameter. That doesn't seem like a whole lot considering how women's bony pelvic dimensions can vary more than that. Still, these data suggest that the difference between a relatively low risk of having a c-section and a relatively high risk of having a c-section amounts to less than a centimeter in fetal head diameter. And maybe it does. Nobody's saying that big heads aren't a major problem sometimes! But maybe there's something else to consider that the paper absolutely didn't.
Neonatal heads get squeezed and molded into interesting shapes in the birth canal.
The data say that normal HC babies get born vaginally more often than large HC ones. But this is based on the head measures of babies who are already born! If we're pitting head circumference (HC) of babies plucked from the uterus against the HC of babies who've been through hello! then of course the vaginally delivered ones could have smaller HCs.
C-sected babies tend to have rounder heads than the ones squeezed by the birth canal. It's impossible to know but I'm fairly confident about this, at least for a subsample of a population: Birth the same baby from the same mother both ways, vaginally and surgically, and its head after c-section will have a larger HC than its squeezed conehead will after natural birth.
 |
| Measuring newborn head circumference (HC). source |
If these thoughts about neonatal cranial molding are worthwhile, then here we have a seemingly useful and very high-profile professional study, grounded in the popular but deeply flawed obstetrical dilemma hypothesis, that is arguing for medical intervention in childbirth based solely on the difference in head size measures induced by those very medical interventions.
The circle of life!
Özelsin , Güzelsin ,Güzeliz...
İyi akşamlar sevgili Kuğucanlarım ,sessizliğime sesim ,bulutlu günlerime güneşlerim , İnşallAh iyisinizdir. Sessiz dostlarımız ,hayvanlara bir kap su bir kap yemek kampanyam çok güzel gidiyor ve çok güzel tepkiler alıyorum ,destekleriniz için çok teşekkür ederim ben o hayvanlara bir damla suyu pay edemem ama birlik olursak hepimizin birer damlası baraj olur , desteğiniz için çok teşekkür ediyorum ve bu dahada büyüyecek dillendirdikçe bilinçleneceğiz bilmediklerimizi öğreneceğiz sizi güzel dostlarla tanıştırayım, işte buyurun;
Ben KurabiyeciMiss Zehra Ertuğru size bahsettigim mama kaplarının resimleri aşağıdadır. Dilerim yardımcı olabilmişimdir. Sevgilerimle.
Birlikten kuvvet doğar,bu arada fidanlar aşılanmış :))
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River;
Küçüklüğümden beri kuşları seven biri olmuşumdur, daha ufaklığımdan kalma bir alışkanlığım vardır, her sabah kalkar camın önüne biraz buğday alır koyar, güvercinlerin doluşmasını izlerdim. Dim dememde ki etken, yan komşumuzun şikayeti ve dahası kuşlar gelmesin diye camın önüne poşet asıp korkutmaya çalışmasından sonra bir müddet şikayet üzerine bırakmak zorunda kalmıştım. Ama bu defa da evden çıkarken pencere kenarı yerine apartman kapısının önüne bırakma alışkanlığım başlamıştı.
Sabah blogunda etkinliği okuyunca lise zamanlarım aklıma geldi, ne denli evin kapısına biraz buğday bırakma alışkanlığım devam etse de dayanamayıp pencere kenarına da ilave ettim ve biraz da nostaljik ruh haline büründüm açıkçası. ^^
Ne var ki bu kez eskisi gibi güvercinlerle doluşması penceremin önü, yine de gelen bir ziyaretçi beni memnun etmeye yetip arttı. :)
Tül perdenin arkasına dikkat!
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Kirpik Kız;
bloğum yok ama sokak hayvanları konusunda oldukca hassas biriyimdir,bahçeli bi evde oturduğumdan kedimiz köpeğimiz eksik olmaz :)
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Neşeli kitap vagonu
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ECE EVREN;
LÜTFEN ONLARIN DA ACIYAN BİR CANLARI OLDUĞUNU,ACIKIP SUSADIKLARINI VE BİZLERE EMANETEN BIRAKILDIKLARINI UNUTMAYALIM. BUNLARI YAPAMIYORSAK EN AZINDAN İNSAFLI BİR GÖZLE BAKALIM. HESAPLARIMIZI KABARTMAYALIM.LÜTFENNN...
Ponçiği
..........................................................................................................................................................................
Bir yolcu;
O her zaman herkese dost kocaman bir kalbi var insanlar ve hayvanları çok seviyor....
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Sıcacık son dakika bir Mail geldi bakın bakalım kimden:)
Cafe tigris;
benim annem de tam bir kuş delisi yıllardır pencerenin önünde beslediği güvercinlerin, kumruların torunlarının torunları olmuştur .
Benim de küçük balkonumda yumurtadan çıkıp uçan dört güvercin bebeğim oldu . Haaa bir de bizim aspiratör borusunu mesken eden serçelerimiz var onların resmini çekemedim ;))
Sizde bana resim , öykülerinizi , hikayelerinizi yada sadece bir şeyler yazmak , yollamak isterseniz;
one.black.svvan@gmail.com
Şimdiden Hayırlı Cuma'lar
Sevgilerimle...
Humans are master meaning generators
 |
A hashtag in the sky above a school at dusk in southern Rhode Island. Was it put there intentionally? What does it mean? |
For as long as we’ve been writing about exquisite Paleolithic cave paintings and carefully crafted Stone Age tools we’ve been debating their meanings. And the debate carries on because meaning is difficult to interpret and that’s largely because “what does it mean?” is a loaded question.
“Meaning” is a hallmark of humanity and, as the thinking often goes, it is a unique aspect of Homo sapiens. No other species is discussing meaning with us. We’re alone here. So we’re supposed to be at least mildly shocked when we learn that Neanderthals decorated their bodies with eagle talons. And it’s supposed to be even harder to fathom that Neanderthals marked symbolic thinking on cave walls. But such is the implication of lines marked by Neanderthals in the shape of a hashtag at Gibraltar.
 |
| source: "The Gibraltar Museum says scratched patterns found in the Gorham’s Cave, in Gibraltar, are believed to be more than 39,000 years old, dating back to the times of the Neanderthals. Credit: EPA/Stewart Finlayson" |
This sort of meaningful behavior, combined with the fact that many of us are harboring parts of the Neanderthal genome, encourages us to stop seeing Neanderthals as separate from us. But another interpretation of the hashtag is one of mere doodling; its maker was not permanently and intentionally scarring the rock with meaning. These opposing perspectives on meaning, whether it’s there or not, clash when it comes to chimpanzee behavior as well.
We’ve grown comfortable with the ever-lengthening list of chimpanzee tool use and tool-making skills that researchers are reporting back to us. But a newly published chimpanzee behavior has humans scratching their heads. Chimpanzees in West Africa fling stones at trees and hollow tree trunks. The stones pile up in and around the trees, looking like a human-made cairn (intentional landmark) in some cases. Males are most often the throwers, pant-hooting as they go, which is a well-knownscore to various interludes of chimpanzee social behavior.
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| source: "Mysterious stone piles under trees are the work of chimpanzees.© MPI-EVA PanAf/Chimbo Foundation" |
Until now, chimp behaviors that employ nature’s raw materials—stones, logs, branches, twigs, leaves—have been easy to peg as being “for” a reason. They’re for cracking open nutritious nuts, for stabbing tasty bushbabies (small nocturnal primates), or for termite fishing. But throwing stones at trees has nothing to do with food. If these chimps do it for a reason then it’s a little more esoteric.
Maybe they do it for pleasure, to let off steam, or to display, or maybe they do it because someone else did it. It may be all of those things at once, and maybe so much more. Maybe you’d call that ritual. Maybe you wouldn’t. Maybe you’d say that they do it because that’s what chimps do in those groups: they walk on their knuckles; they eat certain foods; they make certain sounds; they sleep in certain terms in certain trees; and they do certain things with rocks, like fling them in certain places. Maybe we could just say that this behavior is the way of certain chimpanzees, hardly more mystifying than other behaviors that we’ve come to expect of them.
For comparison, I have certain ways. There are piles of books near my desk. They pile up on tables and shelves. I could fling books on the floor but I don’t. I’m not against flinging them on the floor; it’s just not how things are usually done. I share this behavior with many other, but not all, humans in the presence of books, tables, and shelves. Until I wrote this paragraph, I never gave it much thought, it’s not something that factors even remotely into how I see the world or my place in it, and yet the piling of books on tables and shelves is quite a conspicuous and, therefore, large part of my daily life.
So, why isn’t someone setting up a camera trap in my office and writing up “human accumulative book piling” in Nature? Because this type of behavior, whatever it means, is quintessentially human. No one could claim to discover it in a prestigious publication unless they discovered it in a nonhuman. And they did.
Normally what we do when we learn something new about chimpanzee behavior is we end up crossing one more thing off our list of uniquely human traits. “Man the tool-maker” was nixed decades ago. What should we cross off the list now with this new chimp discovery? Would it be “ritual” and by extension “meaning,” or would it be “piling up stuff”? About that Neanderthal hashtag, do we cross off “art” or “symbolism” and by extension “meaning,” or would we just cross off “doodling,” which holds a quite different meaning? Rather than crossing anything off our list, do we welcome Neanderthals into our kind so we can keep our monopoly on hashtags? Whatever we decide, case by case, trait by trait, we usually interpret our shrinking list of uniquely human traits to be clear demonstration that other animals are becoming more human-like the more we learn about the world.
#WhatDoesThatEvenMean #PantHoot #Hashtag #ThisIsMyCaveWall
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