One manifestation of gene-environment interactions is that: environmental adversity can cause (presumably random) DNA mutations in various genes, as well as throughout the entire genome (keeping in mind that ~99% of the genome is DNA that does not code for proteins). “Environmental adversity” includes anything that can be mutagenic (i.e. able to cause a mutation); this could be chemicals in cigarette smoke or occupational exposure, endogenous compounds such as one’s own metabolites, or dietary substances such as metabolites of beefsteak or broccholi. It is well known that somatic cell mutations (as opposed to germ line mutations in ova or sperm) occur in healthy cells throughout our lives. Most of these mutations do not alter cell behavior and simply appear to accumulate passively. Occasionally, however, a key gene is altered in a way that provides a mutant cell with a competitive advantage, leading to the formation of persistent mutant clones of this aberrant cell.
Such clones are thought to be the origin of cancer initiation and have also been linked to other diseases. Despite the importance of somatic mutation, understanding its extent in normal tissues has been challenging, because of the difficulties of identifying mutations present in small numbers of cells. The most highly mutated normal tissue known is sunshine-exposed human skin. Deep-targeted sequencing of sun-exposed skin from middle-aged individuals revealed large numbers of mutant clones under positive selection, with about 25% of skin cells carrying cancer-driving mutations. Because most mutations are caused by ultraviolet (UV) light, it is unclear whether aged sun-exposed skin represents a special case — due to a lifetime of exposure to a powerful mutagen. This question motivated the authors [see attached article & editorial] to investigate the mutational landscape of esophageal epithelium (cells lining the esophagus), a tissue with a similar structure, but exposure to very different mutagens (i.e. dietary substances, instead of sunshine).
Like the skin, esophageal epithelium consists of layers of keratinocytes. Cells are shed from the surface throughout life and are replaced by proliferation of cells near the basement membrane. In addition, both skin — as well as upper and mid-esophagus — can develop squamous cell cancer. Authors [see attached article] mapped mutant clones in normal esophageal epithelium from nine donors (age range, 20 to 75 years). Somatic mutations accumulated with age and were caused presumably by dietary and endogenous metabolic mutational processes. Authors found strong positive selection of clones carrying mutations in 14 cancer genes — with tens to hundreds of clones per square centimeter.
In middle-aged and elderly donors, clones with cancer-associated mutations covered much of the esphageal epithelium, with NOTCH1 and TP53 gene mutations affecting 12 to 80% and 2 to 37% of cells, respectively. Unexpectedly, the prevalence of NOTCH1 mutations in normal esophagus was several times higher than that in esophageal cancers. Authors conclude that their findings have implications for our understanding of cancer and aging. For example, how can all these mutated cells/clones just be “sitting there”, rather than proceeding on to cause many more cancers?
Among cigarette-smoking pregnant mothers, studies have demonstrated an association with increased risk of behavioral disorders — not only in their children, but also in multiple generations of descendants [see Refs. 1–5 of the attached article]. Although maternal nicotine use is a considerable concern, more men smoke cigarettes than women. Some clinical studies have suggested that paternal cigarette smoking might adversely impact the child’s attention span, and might increase risk for attention deficit hyperactivity disorder (ADHD) in offspring.
However, human studies cannot fully separate the effects of paternal smoking from those of genetic and environmental factors. For example, ADHD and nicotine addiction are often seen together, and ADHD does tend to run in families — making it difficult to separate the genetics of paternal ADHD risk from the environment of paternal smoking, when studying behavioral changes in the offspring. Thus, experimental animal model systems can be valuable tools to “tease out” the (paternal) genetic, from the environmental, effect of nicotine on the developing baby while in the uterus, and then behavioral effects seen later in life.
Authors [see attached report] exposed male mice to nicotine (200 μg/mL in drinking water for 12 weeks) and then bred these male mice with “nicotine-naive” females — to produce the F1 generation; this was followed by breeding male and female F1 mice to produce the F2 generation. Both male and female mice in the F1 and F2 generations exhibited significant impairment in a number of behavioral traits, or phenotypes (such as significant increases in spontaneous locomotor activity and significant deficits in reversal learning).
The F1 generation was also found to have significant changes in monoamine neurotransmitter-signaling mechanisms in the brain (significant deficits in attention, brain monoamine content, and dopamine receptor mRNA expression). Analyzing spermatozoal DNA from the nicotine-exposed founder males, authors detected epigenetic modifications (significant changes in global DNA-methylation and DNA-methylation at promoter regions of the dopamine D2 receptor gene). Authors conclude that these alterations in DNA-methylation might be a plausible mechanism for the transgenerational transmission of the nicotine-induced behavioral and neurotransmitter phenotypes that were found.
innate immune responseis a cell-intrinsic defense program that is rapidly up-regulated upon infection in most cell types. It acts to
inhibit pathogen (i.e. virus, bacteria, fungus)
replication, while signaling the pathogen’s presence to other
cells. This program involves modulation of several cellular pathways,
including production of
antiviral and inflammatory cytokines (a broad and loose category of small proteins
such as interferon, interleukin,
and growth factors — that are secreted by certain
cells of our immune system and exert an effect on other cells), up-regulation of
genes that restrict pathogens, and induction of cell death. An important characteristic of the
innate immune responseis the
rapid evolution that many of its genes have undergone
evolutionarily, as one proceeds along the vertebrate lineage. This rapid
evolution is often attributed to pathogen-driven selection (gene-environment
interactions here = genes responding to pathogens as the environment).
Another hallmark of the
innate immune responseis its
high level of heterogeneity among responding cells: there is extensive cell-to-cell variability in response to pathogen infection, as well as to pathogen-associated molecular patterns.
The functional importance of this variability is unclear. These two characteristics —
rapid evolutionary divergence, and large cell-to-cell variability — would seem to be at odds with the
strong regulatory constraints imposed on the host’s immune response. We see this need to
execute a well-coordinated and carefully balanced program, while avoiding tissue damage and
pathological immune conditions. How this tight regulation is maintained, despite
rapid evolutionary divergence
high cell-to-cell variability
remains unclear — but this is
central to our understanding of the innate immune response and its evolution.
innate immune response’s transcriptional divergence (DNA of genes transcribed into messenger RNA)
between species, and the variability in expression among cells. Using
multicellular, versus single-cell, transcriptomics (DNA transcribed into mRNA in fibroblasts and mononuclear phagocytes from different species that
had been challenged with immune stimuli),
authors mapped the
genetic architecture of the innate immune response. Transcriptionally diverging genes
— including those that encode
cytokines and chemokines — vary across cells and have distinct promoter structures (chemokines
= subclass of cytokines with functions that specifically include attracting white blood cells to sites of infection).
genes involved in regulation of the innate immune response— such as those that encode transcription factors and kinases (enzymes
that catalyze transfer of a phosphate group from ATP to another specified molecule)
evolutionarily conserved between species and display low cell-to-cell variability in expression.
Authors suggest that this expression pattern, which is observed across species and conditions, has evolved as a
mechanism for fine-tuned regulation to achieve an effective, but well balanced, response to pathogens.
Just appearing this month in PloS Biol [see attached 2nd article], this article is consistent with what our GEITP pages have been describing the last several days. The human gut microbiome is known to vary extensively between individuals, and this variability is frequently associated with diet, age, sex, and body mass index (BMI) — as well as diseases presenting as health disparities. The overlapping risk factors and burden of many chronic diseases disproportionally affect ethnic minorities in the US. Recent evidence is in agreement with the hypothesis that ethnicity is correlated with variation in microbial abundance, specifically in the microbiomes of the gut, oral cavity, and vagina.
To varying degrees, ethnicity can capture many facets of biological variation — including social, economic, and cultural variation — as well as aspects of human genetic variation and biogeographical ancestry. Ethnicity also serves as a proxy to characterize health disparity incidence in the US and, whereas factors such as genetic admixture create ambiguity of modern ethnic identity, self-declared ethnicity has proven a useful proxy for genetic and socioeconomic variation in population-scale analyses.
By examining associations between ethnicity and differences in two US-based gut microbiota data sets that included 1,673 individuals, authors found 12 microbial genera and families that reproducibly vary by ethnicity. A majority of these microbial taxa — (including the most heritable bacterial family Christensenellaceae) — overlap with genetically associated taxa and form co-occurring clusters linked by similar fermentative and methanogenic metabolic processes. These results demonstrate recurrent associations between specific taxa in the gut microbiota and ethnicity, providing hypotheses for examining specific members of the gut microbiome as mediators of health disparities.
The topic today is a pretty cool example of
gene-environment interactions. As these GEITP pages have discussed before,
the bacteria in our gut (comprising each individual’s ‘MICROBIOME’) account for ~92%
of all “our” DNA (i.e. just 8% of DNA is ‘our own’). Moreover, it is well known that
diet and geographical environment are two principal determinants of microbiome structure and function. Rural indigenous populations have been found to harbor
substantial biodiversity in their gut microbiomes — which sometimes even include
novel microbial taxa (each ‘taxon’ is an evolutionary
group of one or more populations of an organism, categorized by taxonomists to
form a unit) not found in industrialized populations. This loss of indigenous microbes or ‘‘disappearing microbiota’’ (in
migrants now living on a different country) may be important in explaining the
rise of chronic diseases in the modern world. Despite the
frequent migration of people across national borders in an increasingly
interconnected world, little is known about
how human migration affects one’s individual microbiome.
The United States hosts the largest number of immigrants in the world (49.8
million; 19% of the world’s total immigrants and ~21% of the U.S. population). Epidemiological evidence shows that residency in the US increases risk of obesity and other chronic diseases
among immigrants (compared with individuals of the same ethnicity that continue to reside in their country of birth);
indeed, some groups exhibit as much as a 4-fold increase in obesity after 15 years at their new location. In addition to
Latinos migrating northward, Southeast Asian refugees display the highest average increases in
body mass index (BMI) after relocation to the US. The
Hmong, a minority ethnic group from China (i.e. SE Asia),
make up the largest refugee group in Minnesota; the Karen, an ethnic minority from Burma, have been arriving
in large numbers in more recent years. Overweight status and obesity rates are highest among
Karen, compared with other Asian ethnic groups in Minnesota. Changing to Western diet, previous history of malnutrition,
and physical inactivity —
have been suggested as contributing factors.
attached article] analyzed
514 Hmong and Karen individuals living in Thailand versus
the US, including
first- and second-generation immigrants and 19 Karen individuals sampled before and after immigration, as well as from
36 US-born European-American individuals. By means of DNA sequencing of gut bacteria,
authors found that migration from a non-Western country to the US is associated with
immediate loss of gut microbiome diversity and function in which US-associated strains and functions
displace native strains
functions. These effects were found to
increase, as a function of the duration of US residence, and these effects are
compounded by obesity and across generations.
COMMENT: Dan, I believe that “duration of US residence,” especially for children, would translate directly into “likelihood of (probably unnecessary) trearment with oral antibiotics in this country” — which will devastate your gut microbiome. The result in these kids would mean much less diverse population of microbiota.
As most of us have heard repeatedly over the past 10+ years,
something is affecting
pollinator-bee survival. This is clearly a topic for these GEITP pages (i.e.
gene-environment interactions). Bees are critical contributors to agricultural crop production
— as well as to the life cycle of most
flowering plant species on the planet. Yet, these essential ecosystem-service providers appear to be in decline.
Widespread pesticide use, associated with increasingly intensive agriculture, is one of several (likely interacting)
factors that contribute to these concerns about the drop in number of pollinator insects.
insecticide applications are targeted at controlling pests, their use can have
unintended impacts on beneficial insects, including bees. As the most widely used class of insecticides in the world
today, neonicotinoids have come under considerable scrutiny, following concerns around their nontarget impacts
on bees. Authors [see
and editorial] identified how exposure to one
of these neurotoxic insecticides
can adversely affect individual bumblebees
and social dynamics within their colony.
Using an innovative automated robotic platform,
authors continuously monitored the behavior of
uniquely identified workers inside multiple bumblebee colonies, each housed in a
specially-constructed nest box attached to a foraging chamber. They found that “environmentally realistic”
exposure to the neonicotinoid imidacloprid [chemical
structure seems likely to interact by binding covalently with endogenous compounds],
in artificial nectar collected from the foraging chamber, resulted in
measurable behavioral changes in workers inside the nest.
The bumblebees were
less active, less likely to feed and care for larvae (i.e. to act as nurses),
and more likely to be found toward the nest periphery, when compared with worker bees in control colonies. Curiously, these
changes were more pronounced at night, which might reflect the bees’
daily patterns in pesticide consumption
of this chemical.
Previous GEITP pages have noted that
ancestral Amerindianshad diverged from
Siberian and East Asian populations around 25,000 ± 1100 years ago, followed by a split (divergence of ancestral Amerindians from Ancient Beringians)
between 22,000 and 18,000 years ag). Subsequently, Amerindians diverged (between ~17,500 and 14,600 years ago)
into two branches — Northern Native Americans and Southern Native Americans. All
contemporary and ancient Amerindian individuals, for whom genome-wide data have been generated, prior to the
present study [attached], are derived from either the
North American or South American branch.
However, there is disagreement over claims of earlier migrations into the Americas
— possibly related to
Australasians or by bearers of a distinctive cranial form (“Paleoamericans”).
Whether there were additional splits within the Americas, how many migratory movements north and south took place, and the
speed of human dispersal at different times and regions — are
still being debated. Overall, the degree of
population isolation, admixture, or continuity in different geographic regions of the Americas, after initial settlement, is
Authors in the
1st publication [see attached] sequenced
15 ancient human genomes — spanning from Alaska to Patagonia; six
were ≥10,000 years old. All were most closely related to Native
Americans, including an Ancient Beringian individual, and two
morphologically distinct “Paleoamericans.”
evidence of rapid dispersal and early diversification (including previously unknown groups),
as people moved south. This resulted in multiple, independent, geographically uneven, migrations — including one that
provides clues of a Late Pleistocene
(18,000 to 11,700 years ago)
Australasian genetic signal, and a later Mesoamerican-related (1500
B.C. to 300 A.D.) expansion. These results portray the complex and dynamic population histories from North to South America..!!
2nd study [attached],
authors report genome-wide ancient DNA from
49 individuals —
four parallel time transects in Belize, Brazil, the Central Andes, and the Southern Cone —
each dating to at least ~9,000 years ago.
The common ancestral population radiated rapidly from just one of the
two early branches that contributed to Native Amerindians today.
two previously unappreciated streams of gene flow between North and South America: one affected the
Central Andes by ~4,200 years ago, whereas the other explains an affinity between the oldest North American genome (from what is now New Mexico)
associated with the Clovis culture
oldest Central and South Americans from Chile, Brazil, and Belize.
However, this was not the primary source for later South Americans,
because the other ancient individuals are the result of lineages without
specific affinity to the Clovis-associated genome.
These data suggest a population replacement that began at least 9,000
years ago and was followed by substantial (complicated)
population continuity in multiple regions. 🙁
Over the past decade, these GEITP pages have covered many
genome-wide association studies (GWAS), because these projects are attempts to detect DNA sequence variants
that are (statistically significantly) associated with
complex diseases (e.g. type-2 diabetes, schizophrenia, obesity, cancer) or
responses to drugs or environmental toxicants (e.g. efficacy, therapeutic failure, toxicity)
or quantitative traits (e.g. height, body mass index, IQ). And GWAS results have indeed
identified thousands of genetic variants that are associated with these various multiplex phenotypes. However, the
single-nucleotide polymorphisms (SNPs) or
variants (SNVs) –– that have been identified –– only explain a
risk of disease, or drug or toxicant effect, or quantitative trait;
we now realize that most of the genetic contribution to human
multifactorial traits still remains unidentified. This has been termed
It seems likely that other
molecular factors (e.g. epigenetic effects and protein biomarkers)
are associated with
these multifactorial traits. Whereas biomarkers are often considered
being “markers of disease”, epigenetic factors are suggested to have a
causal effect on disease development (and,
most likely, other complex and quantitative traits).
Epigenome-wide association studies (EWAS) have therefore been performed –– attempting to identify associations
between epigenetic modifications and complex traits; for example, differential epigenetic patterns have been identified (e.g. for asthma, obesity and
myocardial infarction). However,
it remains unclear whether epigenetic variation is causal in the pathogenesis of disease.
variation can be influenced by genetic variation, binding of
transcription factors, or by environmental factors such as smoking.
Transgenerational heritability of epigenetic alterations (i.e. epigenetic changes that escape reprogramming during formation of gametes and during embryogenesis)
has not been demonstrated in humans. On the other hand, intergenerational heritability has (e.g. a fetus exposed to a certain environment before birth, causing
epigenetic changes during this time).
Newborns of mothers who have smoked during pregnancy exhibit epigenetic
changes that reflect increased activity of specific genes (including
encoding aryl-hydrocarbon receptor repressor) that are involved in metabolism of toxic
components in tobacco smoke; these changes in AHRR
are similar to those observed among adult smokers. However, activity of these genes is
not likely to causeincreased risk of smoke-associated disorders; rather,
exposure to toxic compounds during embryogenesis could serve as a causal factorfor increased disease risk (e.g. variation in
As these GEITP pages have stated many times,
epigenetic effects include DNA-methylation,
histone modifications, and
chromatin remodeling. Assays are now available for the first two, but research is still being refined to create assays
for the latter two. The current article is about DNA-methylation.
SNV data and DNA-methylation data with measurements of protein biomarkers
inflammation or cardiovascular disease –– to investigate the relative contribution of genetic and epigenetic variation on biomarker levels (121 protein
biomarkers were analyzed, relative to DNA-methylation at 470,000 genomic positions, and to more than 10 million SNVs). GWAS and EWAS were performed, using between 698 and 1,033 samples
(depending on data availability for the different phenotypes). Most GWAS loci were
near or inside the gene)
regulatory, whereas most EWAS loci were located in
far away from the nearest gene).
All EWAS signals that overlapped with a GWAS locus were driven by underlying genetic variants, and three EWAS signals were confounded by smoking.
some cis-regulatory SNVs for biomarkers
appeared also to have an effect on DNA-methylation levels,
cis-regulatory SNVs for DNA-methylation
were not found to affect biomarker levels. Associations between protein biomarker and DNA-methylation levels were
seen at numerous loci throughout the genome. Authors conclude that these associations likely reflect underlying
patterns of genetic variants, or specific environmental exposures, or these patterns might represent
secondary effects to the pathogenesis of disease. Without a doubt,
many future (large cohort)
EWAS are predicted. 🙂
Posted inCenter for Environmental Genetics|Comments Off on The relative contribution of DNA-methylation and genetic variants on protein biomarkers for human diseases
In reading the solar data, what we are after in the near term is the likely month of minimum for the
Solar Cycle 24/25 minimum
amplitude of Solar Cycle 25. Of course that quest for truth gets easier
as we approach the minimum, at least apparently. Solar Cycle 24 looks
like being unusual in being short while being weak
and Solar Cycle 25 looks like being a repeat of Solar Cycle 24 in terms
The concept of the Super
Grand Solar Deepest Minimum is fashionable again for the moment. There
is no sign of that in the data. That said, activity in
Solar Cycle 24 was back-loaded and, if the solar activity to
atmospheric temperature connection is real, the planet’s temperature
will be running warmer for a few more years as a consequence of that.
Figure 1: Hemispheric sunspot area and F10.7 flux 1985 – 2018
Sunspot area equates to the F10.7 flux. The solar hemispheres have different trends in activity which can hold for decades.
What causes this is a known unkown in solar science.
Figure 2: Heliospheric Current Sheet Tilt Angle 1976 – 2018
The solar cycle is not
over until the heliospheric current sheet flattens. Figure 2 shows that
activity has popped out of the wedge shape it has been –– in pointing to
Sept 2019 as the month of flattening. If this cycle ends up like Solar Cycle 23,
then that means the decline will steepen up to get to the same point.
The month of flattening, and thus the true change of one cycle to the
next, does not necessarily
coincide with the month of minimum derived from the F10.7 flux:
Figure 3: Heliospheric Current Sheet Tilt Angle aligned on month of minimum
Solar Cycle 23 was an outlier in terms of length in the modern instrument record.
Solar Cycle 24 is tracking along with Solar Cycles 21 and 22 and looks like it’ll be 10 years long.
Figure 4 shows one of the signs that
the Modern Warm Period ended in 2006. The Oulu neutron count parted company from the F10.7 flux which it had tracking closely. Something changed in
Figure 5: Solar Wind Flow Pressure 1967 – 2018
As Figure 4 showed in the
F10.7 flux, solar activity was back-loaded in Solar Cycle 24 with much
higher activity after the solar cycle peak. That effect is more
pronounced in the solar wind flow pressure ––
which is still stronger, so late in the cycle –– than it was
prior to the solar cycle peak in 2014. Note the low and chaotic solar
wind activity during the 1970s cooling period at the beginning of the
A break in trend in 2006 is evident at the end of the Modern Warm Period.
Figure 6: F10.7 Flux and Ap Index 1964 – 2018
Geomagnetic activity was back-loaded in
Solar Cycle 24. The break in 2006 is quite evident.
Figure 7: F10.7 Flux of Solar Cycles 19 to 24 aligned on month of minimum
It looks like Solar Cycle 24 won’t make it to the
average cycle length (seen during the last 300 years) of 11.1 years.
Figure 8: Interplanetary Magnetic Field 1966 – 2018
The interplanetary magnetic field (IMF)
was flat during the 1970s cooling period but still higher than the
average level through Solar Cycle 24. The IMF has been in decline for
the last three decades – paralleling
the decline in sunspot area by hemisphere shown in Figure 10 which we
are following. Just as the activity in hemispheric sunspot area in
Figure 10 has an
upper bound, the IMF over the last three cycles has an apparent lower bound shown by the red line. To get to that line by the solar minimum in
2019 –– will require a rapid decline in activity from here.
Figure 9: Sunspot Area by Hemisphere 1874 – 2018
Because the normal representation of solar activity by sunspot number, or
F10.7 flux, sums the northern and southern hemispheres, that disguises the fact that
the hemispheres have different drivers, or they respond to the same driver differently.
As shown in Figure 9, once the hemispheric activity is disaggregated,
the flatness of activity during the last decades of the Little Ice Age
1930) is evident and the break to a higher level of activity from 1933.
Figure 10: Sunspot Area 1985 – 2018
For a sloppy old ball of
plasma, the Sun shows a lot of discipline. Activity for both hemispheres
has bounced off their respective blue lines above –– which implies some
Figure 11: Sunspot Area 1874 – 1924
Similarly to Figure 10, there was a
four-decade period from the late 19th century during which the
northern solar hemisphere sunspot area was driven by a consistent
Figure 12: Solar Polar Field aligned on minimum for Solar Cycles 22 – 25
The amplitude of the solar polar field strength at solar minimum is predictive of the amplitude of the next solar cycle. After starting out weak, this activity
has been tracking that of the lead-up to Solar Cycle 24 and it looks like
Solar Cycle 25’s amplitude will be much the same. Just as the
summing of the activities of the solar hemispheres is a misleading
compromise, the month of polar field minimum can have a big departure
from the official month of solar cycle maximum –– as
shown in the table following:
There is a case for making
the month of heliospheric current sheet flattening, the month of solar
cycle minimum, and the month of polar field minimum –– as the solar
Figure 13: GISP2 Be10 Data 40,000 BC to 0 AD
Years ago in comments on WUWT, somebody contributed the observation that
climate, in a multidecadal sense, is controlled by the magnetic field from the Sun. The best long term record of that is
the Beryllium 10 (Be10) record from the Antarctic ice sheet. Figure 13 shows that
there are trends in the Be10 record that last tens of thousands of years. The cold spikes of the Older Dryas and Younger Dryas are associated with spikes in Be10, indicating that
a weaker solar
magnetic field allowed galactic cosmic rays to flood into the inner
planets of the solar system, collide with nitrogen atoms in the upper
atmosphere, and produce the spikes in the record.
Figure 14: Dye 3 Be10 record 1424 to 1985
If the solar magnetic field causes changes in climate, then
the warmth of the
last eighty-odd years of the Modern Warm Period should be associated
with a low in Be10 relative the centuries of the Little Ice Age.
Figure 14 shows that this is evident in the Be10 record. The spikes of the Sporer, Maunder and Daltona minima are evident, particularly the ultra-cold decade of the 1690s.
And so is the break in activity from the Little Ice Age to the Modern Warm Period in 1933.
Figure 15: aa Index 1868 – 2018
Our longest geomagnetic
record as measured by instruments is the aa Index. This also shows the
clear breaks at the beginning and end of the Modern Warm Period. The
recent peak was 26.9 in September 2015. Activity
in Solar Cycle 24 was back-loaded to the second half of the cycle.
Figure 16: Cumulative aa Index against the long term average 1868 – 2018
The changes in activity
level associated with the beginning and end of the Modern Warm Period
are confirmed by plotting the cumulative aa-Index against the long-term
average of the record.
Figure 17: aa-Index plotted against Northern Hemisphere temperature lagged by six years
One of the reasons that
this planet is so pleasant to live on –– is that climate does not
instantly respond to changes in solar activity; effects are smoothed and lagged. The correlation between the aa-Index
and northern hemisphere suggests that the lag correlation peaks at six years.
Figure 18: North Atlantic transect 59N to 800 metres depth
This is a graph prepared
by Professor Ole Humlum from his Climate4you site. The diagram was last
updated on August 13, 2018 with Argo data to June 2018. The six-year lag
is hard to tease from the modern temperature
record –– where it is clouded by circulation oscillations. But there is
a calorimeter covering 70% of the Earth’s surface, which is not
affected so much by things that happen in the atmosphere. Figure 18
a lag of eight years from the end of the Modern Warm Period to lower temperatures in the North Atlantic.
Figure 19: Average temperature along 59 N, 30-0W, 0-800m depth
This area corresponds to
the main part of the North Atlantic current. This is also a graph
prepared by Professor Ole Humlum from his Climate4you site. The diagram
was last updated on August 13, 2018 with Argo data
to June 2018. Temperature started trending down, from the end of the
Modern Warm Period in 2006, but really dived down, once the eight-year
lag kicked in, during 2014.
Figure 20: 800,000 years of Antarctic CO2 data relative to plant growth response
Some people have been
tearful recently because atmospheric carbon dioxide levels are much
higher now than their range over the last 800,000 years –– as shown by
ice core data from Antarctica. For some strange reason,
they think this is a bad thing when the opposite is true. As the paper Plant
responses to low CO2 of the past shows, plant growth has responded to the higher atmospheric carbon dioxide level since the start of the Industrial Revolution.
Each 1 ppm increase from here raises plant productivity by 0.3 percent. No wonder world grain production is continuing to rise ––even though the big
increases from genetics are behind us now.
Figure 20 shows what the
amount of plant growth over the last 800,000 years would have been,
relative to the atmospheric concentration (of 368 ppm) at the time the
paper was written.
The current human population of the planet of 7.7 billion couldn’t be sustained at the levels of the last 800,000 years. I have helpfully annotated
the graphic with zones of relative safety. We are now at the beginning of the safe zone with the current concentration of 408 ppm.
Heaven help us –– when the atmospheric concentration starts falling
again –– as it will, when we run out of fossil fuels and the oceans
continue their remorseless 800-year turnover, taking most of our
hard-won CO2 down into the deep oceans, where it will
be no use to either man or beast.
Earth BioGenome Project— a VERY AMBITIOUS $4.7 billion, 10-year plan to
sequence the genomes of all of Earth’s
1.5 million known species of animal, plant, fungus, and protozoan — officially began the first week of November 2o18 [see
the attached brief announcement].
In addition to “providing insight into evolution”, the genomic data are
intended to “help conserve and restore biodiversity”, as well as
for agricultural and biomedical research”.
Wellcome Sanger Institute in Hinxton, U.K.; BGI in Shenzhen, China; and
15 other institutions have pledged to raise $600 million initially and
agreed to coordinate their efforts and to work with other sequencing
projects (to avoid overlap in species covered, for example).
of these affiliated projects will concentrate on taxonomic groups —
such as insects or fungi — but some will focus on the species of
countries. For example, the Wellcome Trustannounced plans to sequence within 10 years
all 66,000 known eukaryotic [organisms consisting of one or more cells in which the genetic material (i.e. DNA) in the form of chromosomes contained
within a distinct nucleus. Eukaryotes include all living organisms other than the eubacteria and archaebacteria]
species in the United Kingdom.
Science 2 Nov 2o18; 362: p. 504
COMMENTS: This seems a bit parochial. Or naïve. Not all species on the planet have even been found/identified yet. In addition, Mother Nature is constantly altering/mutating the species that presently exist.
COMMENT: Well, so much for “easing the tensions” between big-science funding versus little (investigator-initiated) science funding.