Election outcomes are notoriously difficult to predict. In 2016, for example, most polls suggested that Hillary Clinton would win the presidency, but Donald Trump defeated her. Researchers cite multiple explanations for the unreliability in election forecasts—some voters are difficult to reach, and some may wish to remain hidden. Among those who do respond to surveys, some may change their minds after being polled, while others may be embarrassed or afraid to report their true intentions.
In a new perspective piece for Nature, Santa Fe Institute researchers Mirta Galesic, Jonas Dalege, Henrik Olsson, Daniel Stein, Tamara van der Does, and their collaborators propose a surprising way to get around these shortcomings in survey design—not just in the world of politics, but in other types of research as well. While it’s widely assumed that cognitive bias clouds our assessment of the people around us, their research and that of others suggests that in fact, our estimations of what our friends and family believe are often accurate.
“We realized that if we ask a national sample of people about who their friends are going to vote for, we get more accurate predictions than if we ask them who they’re going to vote for,” says Galesic, who is the corresponding author. “We found that people are actually pretty good at estimating the beliefs of people around them.”
That means researchers can gather highly accurate information about social trends and groups by asking about a person’s social circle rather than interrogating their own individual beliefs. That’s because as highly social creatures, we have become very good at sizing up those around us—what researchers call “social sensing.”
When people are selected to represent a particular group, their perceptions, combined with new computational models of human social dynamics, can be used to identify emerging trends and better predict political and health-related developments in particular, the team writes. This approach, combining elements of psychology and sociology, can even be harnessed to devise interventions that “could steer social systems in different directions” after a major event, such as a natural disaster or a mass shooting, they suggest.
“I really hope human social sensing will be included in the standard social science toolbox, because I think it can be a very useful strategy for predicting and modeling societal trends,” Galesic says.
Researchers look to human ‘social sensors’ to better predict elections and other trends (2021, June 30)
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Stone tools have been made by humans and their ancestors for millions of years. For archaeologists, these rocky remnants—lithic artifacts and flakes—are of key importance. Because of their high preservation potential, they are among the most common findings in archaeological excavations. Worldwide, numerical dating of these lithic artifacts, especially when they occur as surface findings, remains a major challenge. Usually, stone tools cannot be dated directly, but only when they are embedded in sediment layers together with, for example, organic material. The age of such organic material can be constrained via the radiocarbon technique. If such datable organic remains are missing or if stone artifacts lack a stratified sedimentary context, but rather occur as scattered surface artifacts, numerical dating becomes very difficult or is simply impossible.
“The Earth’s surface is highly dynamic and erosion and redeposition of material, especially over long timescales, is common. A precise age determination of lithic artifacts that occur as surface finds has therefore hardly been possible so far. Many aspects of ancient human behavior have only been preserved as surface finds, hence cannot be dated precisely with currently available dating methods. By further developing the Optically Stimulated Luminescence (OSL) dating technique, we can now, for the first time, carry out precise, and direct age measurements on lithic artifacts. In our current study we used stone artifacts from an archaeological surface site in south-central Tibet,” explains Michael Meyer, head of the Luminescence Laboratory at the Department of Geology at the University of Innsbruck and one of the main authors of the study now published in the renowned journal Science Advances.
OSL dating is based on the measurement of light stored in natural minerals and is one of the most important absolute dating tools in archaeology and the earth sciences. “This dating method uses natural light signals that accumulate over time in natural dosimeters, such as quartz and feldspar grains that are important constituents of sediments, as well as rocks and lithic artifacts. These minerals can be imagined as miniaturized clocks. Each grain is a tiny clock that can be ‘read-out’ under controlled laboratory conditions. The light signal allows us to infer the age of the archaeological sediment layer or artifact. The more light, the older the sample,” says the geologist. “In this study, we have now taken a new approach and focused not on sediment grains of sand, but—for the first time—on stone artifacts themselves.”
Quarrying activities more than 5,000 years ago
Due to its extreme environmental and climatic conditions the dry highlands of Tibet are considered to be one of the last regions on earth that were occupied by humans. When exactly peopling of this remote and rather extreme environments occurred has caused a lot of scientific debate over the course of the last decade. In 2017, Michael Meyer dated the famous human foot and hand prints of Chusang in the central part of the Tibetan plateau to an age between 8,000 and 12,000 years.
In the current study, Meyer and his team analyzed archaeological finds from southern Tibet in the Innsbruck OSL Laboratory: The excavation site Su-re is located immediately north of the Mount Everest-Cho Oyu massif in the so-called Tingri graben at an elevation of 4450 meters. Surface artifacts are particularly common in Tibet. To date them, the researcher used the so-called rock surface burial dating technique and applied it to lithic surface artifacts. This method determines the point in time when the stone artifact was discarded by humans and at least partly covered by earth.
“With our luminescence method, we can look inside the stone and create a continuous age-depth profile. The inside of a rock has never been exposed to sunlight, so we have a saturated luminescence signal there and an infinite high age. However, if the rock surface is exposed to daylight for a long enough time, the signal in the top millimeters or centimeters of the rock will be erased. This happens during knapping, when the stone tool is produced, and also during the subsequent artifact use by humans. When the artifact is then discarded and at least partially buried in sediment and shielded from light, the luminescence signal in this artifact surface recharges. By measuring this depth-dependent luminescence signal in the rock surfaces, we can calculate the age of the artifact discard, taking into account the dynamics of local earth surface processes. Such an approach allows us to date stone artifacts directly, even if they occur as surface finds,” Meyer explains.
The analyses on the surface artifacts from southern Tibet revealed an age between 5,200 and 5,500 years. “We assume that the artifact findings at Su-re are related to quarrying activities at this site.” Very old sites have been discovered in the central part of the Plateau, however, for southern sector of the Tibetan Plateau, Su-re is currently to oldest securely dated site.
For Michael Meyer, the analysis of these Tibetan artifacts is just the beginning: “This OSL-based method opens up new vistas in archaeological dating and holds great potential also for sites on other continents that preserve lithic artifacts in a favorable setting,” concludes the geologist.
Multi-disciplinary researchers at The University of Manchester have helped develop a powerful physics-based tool to map the pace of language development and human innovation over thousands of years—even stretching into pre-history before records were kept.
Tobias Galla, a professor in theoretical physics, and Dr. Ricardo Bermúdez-Otero, a specialist in historical linguistics, from The University of Manchester, have come together as part of an international team to share their diverse expertise to develop the new model, revealed in a paper entitled ‘Geospatial distributions reflect temperatures of linguistic feature’ authored by Henri Kauhanen, Deepthi Gopal, Tobias Galla and Ricardo Bermúdez-Otero, and published by the journal Science Advances.
Professor Galla has applied statistical physics—usually used to map atoms or nanoparticles—to help build a mathematically-based model that responds to the evolutionary dynamics of language. Essentially, the forces that drive language change can operate across thousands of years and leave a measurable “geospatial signature”, determining how languages of different types are distributed over the surface of the Earth.
Dr. Bermúdez-Otero explained: “In our model each language has a collection of properties or features and some of those features are what we describe as ‘hot’ or ‘cold’.
“So, if a language puts the object before the verb, then it is relatively likely to get stuck with that order for a long period of time—so that’s a ‘cold’ feature. In contrast, markers like the English article ‘the’ come and go a lot faster: they may be here in one historical period, and be gone in the next. In that sense, definite articles are ‘hot’ features.
“The striking thing is that languages with ‘cold’ properties tend to form big clumps, whereas languages with ‘hot’ properties tend to be more scattered geographically.”
This method therefore works like a thermometer, enabling researchers to retrospectively tell whether one linguistic property is more prone to change in historical time than another. This modelling could also provide a similar benchmark for the pace of change in other social behaviours or practices over time and space.
“For example, suppose that you have a map showing the spatial distribution of some variable cultural practice for which you don’t have any historical records—this could be be anything, like different rules on marriage or on the inheritance of possessions,” added Dr. Bermúdez-Otero.
“Our method could, in principle, be used to ascertain whether one practice changes in the course of historical time faster than another, ie whether people are more innovative in one area than in another, just by looking at how the present-day variation is distributed in space.”
The source data for the linguistic modelling comes from present-day languages and the team relied on The World Atlas of Language Structures (WALS). This records information of 2,676 contemporary languages.
Professor Galla explained: “We were interested in emergent phenomena, such as how large-scale effects, for example patterns in the distribution of language features arise from relatively simple interactions. This is a common theme in complex systems research.
“I was able to help with my expertise in the mathematical tools we used to analyse the language model and in simulation techniques. I also contributed to setting up the model in the first place, and by asking questions that a linguist would perhaps not ask in the same way.”
Henri Kauhanen et al, Geospatial distributions reflect temperatures of linguistic features, Science Advances (2021). DOI: 10.1126/sciadv.abe6540
Pace of prehistoric human innovation could be revealed by ‘linguistic thermometer’ (2021, January 27)
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Scientists used human white blood cell membranes to carry two drugs, an antibiotic and an anti-inflammatory, directly to infected lungs in mice.
The nano-sized drug delivery method developed at Washington State University successfully treated both the bacterial growth and inflammation in the mice’s lungs. The study, recently published in Communications Biology, shows a potential new strategy for treating infectious diseases, including COVID-19.
“If a doctor simply gives two drugs to a patient, they don’t go directly to the lungs. They circulate in the whole body, so potentially there’s a lot of toxicity,” said Zhenjia Wang, the study’s corresponding author and an associate professor in WSU’s College of Pharmacy and Pharmaceutical Sciences. “Instead, we can load the two types of drugs into these vesicles that specifically target the lung inflammation.”
Wang and his research team have developed a method to essentially peel the membrane from neutrophils, the most common type of white blood cells that lead the body’s immune system response. Once emptied, these membranes can be used as nanovesicles, tiny empty sacks only 100 to 200 nanometers wide, which scientists can then fill with medicine.
These nanovesicles retain some of the properties of the original white blood cells, so when they are injected into a patient, they travel directly to the inflamed area just as the cells would normally, but these nanovesicles carry the medicines that the scientists implanted to attack the infection.
In this study, first author Jin Gao, a WSU research associate, loaded the nanovesicles with an antibiotic and resolvinD1, an anti-inflammatory derived from Omega 3 fatty acids, to treat lungs infected by P. aeruginosa, a common potentially fatal pathogen patients can catch in hospital settings. The researchers used two drugs because lung infections often create two problems, the infection itself and inflammation created by a strong immune system response.
Toxicity studies and clinical trials would have to be conducted before this method could be used in human patients, but this study provides evidence that the innovation works for lung inflammation. If the method is ultimately proven safe and effective for humans, Wang said the nanovesicles could be loaded with any type of drug to treat a range of infectious diseases, including COVID-19.
“I think it’s possible to translate this technology to help treat COVID-19,” said Wang. “COVID-19 is a virus, not a bacterial pathogen, but it also causes an inflammation response in the lung, so we could load an antiviral drug like remdesivir into the nanovesicle, and it would target that inflammation.”
Jin Gao et al, Co-delivery of resolvin D1 and antibiotics with nanovesicles to lungs resolves inflammation and clears bacteria in mice, Communications Biology (2020). DOI: 10.1038/s42003-020-01410-5
Medicine-carriers made from human cells can cure lung infections (2020, December 3)
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