Tyler O'Neal, Staff Editor BIG DATA

Early human habitats linked to past climate shifts

A study by an international team of scientists provides clear evidence for a link between astronomically-driven climate change and human evolution.

By combining the most extensive database of well-dated fossil remains and archeological artifacts with an unprecedented new supercomputer model simulating earth’s climate history over the past 2 million years, the team of experts in climate modeling, anthropology, and ecology was able to determine under which environmental conditions archaic humans likely lived (Fig. 1). Preferred habitats of Homo sapiens (purple shading, left), Homo heidelbergensis (red shading, middle), Homo neanderthalensis (blue shading, right) calculated from a new paleoclimate model simulation conducted at the IBS Center for Climate Physics and a compilation of fossil and archeological data. Lighter values indicate higher habitat suitability. The dates (1 ka = 1000 years before present) refer to the estimated ages of the youngest and oldest fossils used in the study.

The impact of climate change on human evolution has long been suspected but has been difficult to demonstrate due to the paucity of climate records near human fossil-bearing sites. To bypass this problem, the team instead investigated what the climate in their computer simulation was like at the times and places humans lived, according to the archeological record. This revealed the preferred environmental conditions of different groups of hominins. From there, the team looked for all the places and times those conditions occurred in the model, creating time-evolving maps of potential hominin habitats.

“Even though different groups of archaic humans preferred different climatic environments, their habitats all responded to climate shifts caused by astronomical changes in earth’s axis wobble, tilt, and orbital eccentricity with timescales ranging from 21 to 400 thousand years,” said Axel Timmermann, lead author of the study and Director of the IBS Center for Climate Physics (ICCP) at Pusan National University in South Korea.

To test the robustness of the link between climate and human habitats, the scientists repeated their analysis, but with ages, the fossils shuffled like a deck of cards. If the past evolution of climatic variables did not impact where and when humans lived, then both methods would result in the same habitats. However, the researchers found significant differences in the habitat patterns for the three most recent hominin groups (Homo sapiens, Homo neanderthalensis, and Homo heidelbergensis) when using the shuffled and the realistic fossil ages. “This result implies that at least during the past 500 thousand years the real sequence of past climate change, including glacial cycles, played a central role in determining where different hominin groups lived and where their remains have been found”, said Prof. Timmermann.

“The next question we set out to address was whether the habitats of the different human species overlapped in space and time. Past contact zones provide crucial information on potential species successions and admixture,” said Prof. Pasquale Raia from the Università di Napoli Federico II, Naples, Italy, who together with his research team compiled the dataset of human fossils and archeological artifacts used in this study. From the contact zone analysis, the researchers then derived a hominin family tree, according to which Neanderthals and likely Denisovans derived from the Eurasian clade of Homo heidelbergensis around 500-400 thousand years ago, whereas Homo sapiens’ roots can be traced back to Southern African populations of late Homo heidelbergensis around 300 thousand years ago.

“Our climate-based reconstruction of hominin lineages is quite similar to recent estimates obtained from either genetic data or the analysis of morphological differences in human fossils, which increases our confidence in the results,” remarks Dr. Jiaoyang Ruan, co-author of the study and postdoctoral research fellow at the IBS Center for Climate Physics.

The new study was made possible by using one of South Korea’s fastest supercomputers named Aleph. Located at the headquarters of the Institute for Basic Science in Daejeon, Aleph ran non-stop for over 6 months to complete the longest comprehensive climate model simulation to date. “The model generated 500 Terabytes of data, enough to fill up several hundred hard disks,” said Dr. Kyung-Sook Yun, a researcher at the IBS Center for Climate Physics who conducted the experiments. “It is the first continuous simulation with a state-of-the-art climate model that covers earth’s environmental history of the last 2 million years, representing climate responses to the waxing and waning of ice-sheets, changes in past greenhouse gas concentrations, as well as the marked transition in the frequency of glacial cycles around 1 million years ago,” adds Dr. Yun.

“So far, the paleoanthropological community has not utilized the full potential of such continuous paleoclimate model simulations. Our study clearly illustrates the value of well-validated climate models to address fundamental questions on our human origins,” says Prof. Christoph Zollikofer from the University of Zurich, Switzerland, and co-author of the study.

Going beyond the question of early human habitats, and times and places of human species’ origins, the research team further addressed how humans may have adapted to varying food resources over the past 2 million years. “When we looked at the data for the five major hominin groups, we discovered an interesting pattern. Early African hominins around 2-1 million years ago preferred stable climatic conditions. This constrained them to relatively narrow habitable corridors. Following a major climatic transition about 800 thousand years ago, a group known under the umbrella term Homo heidelbergensis adapted to a much wider range of available food resources, which enabled them to become global wanderers, reaching remote regions in Europe and eastern Asia,” said Elke Zeller, a Ph.D. student at Pusan National University and co-author of the study.

“Our study documents that climate played a fundamental role in the evolution of our genus Homo. We are who we are because we have managed to adapt over millennia to slow shifts in the past climate,” says Prof. Axel Timmermann.

Japanese researchers use Monte Carlo, MD simulations to predict self-organization of charged Janus particles

Tyler O'Neal, Staff Editor BIG DATA

Researchers from The Research Center for Advanced Science and Technology and The Institute of Industrial Science at The University of Tokyo used a new supercomputer simulation to model the electrostatic self-organization of zwitterionic nanoparticles, which are useful for drug delivery. They found that including transient charge fluctuations greatly increased the accuracy, which may help lead to the development of new self-assembling smart nanomaterials. Researchers at The University of Tokyo used a hybrid of Monte Carlo and molecular dynamics simulations to predict the self-assembly of charged Janus particles, which may lead to biomimetic nanostructures that can assemble like proteins CREDIT Institute of Industrial Science, The University of Tokyo

In ancient Roman mythology, Janus was the god of both beginnings and endings. His dual nature was often reflected in his depiction with two faces. He also lends his name to so-called Janus particles, which are nanoparticles that contain two or more distinct physical or chemical properties on their surface. One promising “two-faced” solution uses zwitterionic particles, which are spheres with a positively charged side and a negatively charged side. Researchers hope to create self-organizing structures, which can be activated by changes in a solution’s salt concentration or pH. However, this kind of “bottom-up” engineering requires more accurate supercomputer simulations to implement.

Now, a team of researchers from The Research Center for Advanced Science and Technology and The Institute of Industrial Science at The University of Tokyo has created a new supercomputer model that incorporates transient fluctuations in the change distributions on the surface of the particles that can give rise to a wider variety of structures, compared with current software. “Simulating the dynamic dissociation or association of ionization groups is inherently more challenging and must be iterated repeatedly until self-consistent results are obtained,” first author Jiaxing Yuan says.

The researchers showed that the previous method of assuming each of the particles carries a constant charge can give inaccurate results. To simulate the possible transition to compact clusters, instead of exclusively producing elongated strands, the computer needed to include short-lived fluctuations in surface charge. These differences are particularly noticeable at low salt concentration and high electrostatic coupling strength.

In living organisms, proteins fold into very specific shapes based in large part on the attraction between the positively and negatively charged regions. In the future, artificially designed particles may be able to self-assemble when triggered by a change in conditions. “With zwitterionic particles, we hope to create functional materials with tunable properties, similar to the self-organization of charged proteins,” senior author Hajime Tanaka says.

Danish researchers develop algo to find possible misdiagnosis

Tyler O'Neal, Staff Editor BIG DATA

It does not happen often. But on rare occasions, physicians make mistakes and may make a wrong diagnosis. Patients may have many diseases all at once, where it can be difficult to distinguish the symptoms of one illness from the other, or there may be a lack of symptoms.

Errors in diagnosis may lead to incorrect treatment or a lack of treatment. Therefore, everyone in the healthcare system tries to minimize errors as much as possible.

Now, researchers at the University of Copenhagen have developed an algorithm that can help with just that.

'Our new algorithm can find the patients who have such an unusual disease trajectory that they may indeed not suffer from the disease they were diagnosed with. It can hopefully end up being a support tool for physicians', says Isabella Friis Jørgensen, Postdoc at the Novo Nordisk Foundation Center for Protein Research. Professor Søren Brunak hopes that the new algorithm could become a support tool for physicians (photo: Peter Hove Olesen){module INSIDE STORY}

The algorithm revealed possible lung cancer

The researchers have developed the algorithm based on disease trajectories for 284,000 patients with chronic obstructive pulmonary disease (COPD), from 1994 to 2015. Based on these data, they came up with approximately 69,000 typical disease trajectories.

"In the National Patient Registry, we have been able to map what you could call typical disease trajectory. And if a patient shows up with a very unusual disease trajectory, then it might be that the patient is simply suffering from a different disease. Our tool can help to detect this," explains Søren Brunak, Professor at the Novo Nordisk Foundation Center for Protein Research.

For example, the researchers found a small group of 2,185 COPD patients who died very shortly after being diagnosed with COPD. According to the researchers, it was a sign that something else might have been wrong, maybe something even more serious.

"When we studied the laboratory values from these patients more closely, we saw that they deviated from normal values for COPD patients. Instead, the values resembled something that is seen in lung cancer patients. Only 10 percent of these patients were diagnosed with lung cancer, but we are reasonably convinced that most, if not all of these patients actually had lung cancer," explains Søren Brunak.

Data that will provide an immediate benefit

Although the algorithm was validated through data from COPD patients, it may be used for many other diseases. The principle is the same: the algorithm uses registry data to map the typical disease trajectories and can detect if some patients' disease trajectories stand out so much that something may be wrong.

"Naturally, our most important goal is for the patients to get their money's worth with respect to their health care. And we believe that in the future, this algorithm may end up becoming a support tool for physicians. Once the algorithm has mapped the typical disease trajectories, it only takes 10 seconds to match a single patient against everyone else," says Søren Brunak.

He emphasizes that the algorithm must be further validated and tested in clinical trials before it can be implemented in Danish hospitals. But he hopes it is something that can be started soon.

 

"In Denmark, we often praise our good health registries because they contain valuable data for researchers. We use them in our research because it may benefit other people in the future in the form of better treatment. But this is actually an example of how your own health data can benefit yourself right away," says Søren Brunak.

Read more about the researchers' new results in the scientific journal NPJ Digital Medicine: ‘Time-ordered comorbidity correlations identify patients at risk of mis- and overdiagnosis’.