Aurelia Butler

They say that where there’s smoke, there’s fire, and Weizmann Institute of Science researchers are working hard to investigate that claim, or at least elucidate what constitutes “smoke.” In an article published today in PNAS, the scientists reveal an advanced, innovative method that they have developed and used to detect nonvisual traces of fire dating back at least 800,000 years — one of the earliest known pieces of evidence for the use of fire. The newly developed technique may provide a push toward a more scientific, data-driven type of archaeology, but — perhaps more importantly — it could help us better understand the origins of the human story, our most basic traditions and our experimental and innovative nature.
The controlled use of fire by ancient hominins — a group that includes humans and some of our extinct family members — is hypothesized to date back at least a million years, to around the time that archaeologists believe Homo habilis began its transition to Homo erectus. That is no coincidence, as the working theory, called the “cooking hypothesis,” is that the use of fire was instrumental in our evolution, not only for allowing hominins to stay warm, craft advanced tools and ward off predators but also for acquiring the ability to cook. Cooking meat not only eliminates pathogens but increases efficient protein digestion and nutritional value, paving the way for the growth of the brain. The only problem with this hypothesis is a lack of data: since finding archaeological evidence of pyrotechnology primarily relies on visual identification of modifications resulting from the combustion of objects (mainly, a color change), traditional methods have managed to find widespread evidence of fire use no older than 200,000 years. While there is some evidence of fire dating back to 500,000 years ago, it remains sparse, with only five archaeological sites around the world providing reliable evidence of ancient fire.
“We may have just found the sixth site,” says Dr. Filipe Natalio of Weizmann’s Plant and Environmental Sciences Department, whose previous collaboration with Dr. Ido Azuri, of Weizmann’s Life Core Facilities Department, and colleagues provided the basis for this project. Together they pioneered the application of AI and spectroscopy in archaeology to find indications of controlled burning of stone tools dating back to between 200,000 and 420,000 years ago in Israel. Now they’re back, joined by PhD student Zane Stepka, Dr. Liora Kolska Horwitz from the Hebrew University of Jerusalem and Prof. Michael Chazan from the University of Toronto, Canada. The team upped the ante by taking a “fishing expedition” — casting far out into the water and seeing what they could reel back in. “When we started this project,” says Natalio, “the archaeologists who’ve been analyzing the findings from Evron Quarry told us we wouldn’t find anything. We should have made a bet.”
Evron Quarry, located in the Western Galilee, is an open-air archaeological site that was first discovered in the mid-1970s. During a series of excavations that took place at that time and were led by Prof. Avraham Ronen, archaeologists dug down 14 meters and uncovered a large array of animal fossils and Paleolithic tools dating back to between 800,000 and 1 million years ago, making it one of the oldest sites in Israel. None of the finds from the site or the soil in which they were found had any visual evidence of heat: ash and charcoal degrade over time, eliminating the chances of finding visual evidence of burning. Thus, if the Weizmann scientists wanted to find evidence of fire, they had to search farther afield.
The “fishing” expedition began with the development of a more advanced AI model than they had previously used. “We tested a variety of methods, among them traditional data analysis methods, machine learning modeling and more advanced deep learning models,” says Azuri, who headed the development of the models. “The deep learning models that prevailed had a specific architecture that outperformed the others and successfully gave us the confidence we needed to further use this tool in an archaeological context having no visual signs of fire use.” The advantage of AI is that it can find hidden patterns across a multitude of scales. By pinpointing the chemical composition of materials down to the molecular level, the output of the model can estimate the temperature to which the stone tools were heated, ultimately providing information about past human behaviors.
With an accurate AI method in hand, the team could start fishing for molecular signals from the stone tools used by the inhabitants of the Evron Quarry almost a million years ago. To this end, the team assessed the heat exposure of 26 flint tools found at the site almost half a century ago. The results revealed that the tools had been heated to a wide range of temperatures — some exceeding 600°C. In addition, using a different spectroscopic technique, they analyzed 87 faunal remains and discovered that the tusk of an extinct elephant also exhibited structural changes resulting from heating. While cautious in their claim, the presence of hidden heat suggests that our ancient ancestors, not unlike the scientists themselves, were experimentalists.
According to the research team, by looking at the archaeology from a different perspective, using new tools, we may find much more than we initially thought. The methods they’ve developed could be applied, for example, at other Lower Paleolithic sites to identify nonvisual evidence of fire use. Furthermore, this method could perhaps offer a renewed spatiotemporal perspective on the origins and controlled use of fire, helping us to better understand how hominin’s pyrotechnology-related behaviors evolved and drove other behaviors. “Especially in the case of early fire,” says Stepka, “if we use this method at archaeological sites that are one or two million years old, we might learn something new.”
By all accounts, the fishing expedition was a resounding success. “It was not only a demonstration of exploration and being rewarded in terms of the knowledge gained,” says Natalio, “but of the potential that lies in combining different disciplines: Ido has a background in quantum chemistry, Zane is a scientific archaeologist, and Liora and Michael are prehistorians. By working together, we have learned from each other. For me, it’s a demonstration of how scientific research across the humanities and science should work.”
Dr. Natalio’s research is supported by the Yeda-Sela Center for Basic Research. More

People are less morally outraged when gender discrimination occurs because of an algorithm rather than direct human involvement, according to research published by the American Psychological Association.
In the study, researchers coined the phrase “algorithmic outrage deficit” to describe their findings from eight experiments conducted with a total of more than 3,900 participants from the United States, Canada and Norway.
When presented with various scenarios about gender discrimination in hiring decisions caused by algorithms and humans, participants were less morally outraged about those caused by algorithms. Participants also believed companies were less legally liable for discrimination when it was due to an algorithm.
“It’s concerning that companies could use algorithms to shield themselves from blame and public scrutiny over discriminatory practices,” said lead researcher Yochanan Bigman, PhD, a post-doctoral research fellow at Yale University and incoming assistant professor at Hebrew University. The findings could have broader implications and affect efforts to combat discrimination, Bigman said. The research was published online in the Journal of Experimental Psychology: General.
“People see humans who discriminate as motivated by prejudice, such as racism or sexism, but they see algorithms that discriminate as motivated by data, so they are less morally outraged,” Bigman said. “Moral outrage is an important societal mechanism to motivate people to address injustices. If people are less morally outraged about discrimination, then they might be less motivated to do something about it.”
Some of the experiments used a scenario based on a real-life example of alleged algorithm-based gender discrimination by Amazon that penalized female job applicants. While the research focused on gender discrimination, one of the eight experiments was replicated to examine racial and age discrimination and had similar findings.
Knowledge about artificial intelligence didn’t appear to make a difference. In one experiment with more than 150 tech workers in Norway, participants who reported greater knowledge about artificial intelligence were still less outraged by gender discrimination caused by algorithms.
When people learn more about a specific algorithm it may affect their outlook, the researchers found. In another study, participants were more outraged when a hiring algorithm that caused gender discrimination was created by male programmers at a company known for sexist practices.
Programmers should be aware of the possibility of unintended discrimination when designing new algorithms, Bigman said. Public education campaigns also could stress that discrimination caused by algorithms may be a result of existing inequities, he said.
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For decades, field-effect transistors enabled by silicon-based semiconductors have powered the electronics revolution. But in recent years, manufacturers have come up against hard physical limits to further size reductions and efficiency gains of silicon chips. That has scientists and engineers looking for alternatives to conventional metal-oxide semiconductor (CMOS) transistors.
“Organic semiconductors offer several distinct advantages over conventional silicon-based semiconducting devices: they are made from abundantly available elements, such as carbon, hydrogen and nitrogen; they offer mechanical flexibility and low cost of manufacture; and they can be fabricated easily at scale,” notes UC Santa Barbara engineering professor Yon Visell, part of a group of researchers working with the new materials. “Perhaps more importantly, the polymers themselves can be crafted using a wide variety of chemistry methods to endow the resulting semiconducting devices with interesting optical and electrical properties. These properties can be designed, tuned or selected in many more ways than can inorganic (e.g., silicon-based) transistors.”
The design flexibility that Visell describes is exemplified in the reconfigurability of the devices reported by UCSB researchers and others in the journal Advanced Materials.
Reconfigurable logic circuits are of particular interest as candidates for post-CMOS electronics, because they make it possible to simplify circuit design while increasing energy efficiency. One recently developed class of carbon-based (as opposed to, say, silicon- or gallium-nitride-based) transistors), called organic electrochemical transistors(OECTs), have been shown to be well-suited for reconfigurable electronics.
In the recent paper, chemistry professorThuc-Quyen Nguyen,who leads the UCSB Center for Polymers and Organic Solids, and co-authors including Visell describe a breakthrough material — a soft, semiconducting carbon-based polymer — that can provide unique advantages over the inorganic semiconductors currently found in conventional silicon transistors.
“Reconfigurable organic logic devices are promising candidates for the next generations of efficient computing systems and adaptive electronics,” the researchers write. “Ideally, such devices would be of simple structure and design, [as well as] power-efficient and compatible with high-throughput microfabrication techniques.”
Conjugating for Conductivity More

When looking at the future of production of micro-scale organic electronics, Mohammad Reza Abidian — associate professor of Biomedical Engineering at the University of Houston Cullen College of Engineering — sees their potential for use in flexible electronics and bioelectronics, via multiphoton 3-D printers.
The newest paper from his research group examines the possibility of that technology. “Multiphoton Lithography of Organic Semiconductor Devices for 3D Printing of Flexible Electronic Circuits, Biosensors, and Bioelectronics” was published online in Advanced Materials.
Over the past few years, 3D printing of electronics have become a promising technology due to their potential applications in emerging fields such as nanoelectronics and nanophotonics. Among 3D microfabrication technologies, multiphoton lithography (MPL) is considered the state-of-the-art amongst the microfabrication methods with true 3D fabrication capability, excellent level of spatial and temporal control, and the versatility of photosensitive materials mostly composed of acrylate-based polymers/monomers or epoxy-based photoresists.
“In this paper we introduced a new photosensitive resin doped with an organic semiconductor material (OS) to fabricate highly conductive 3D microstructures with high-quality structural features via MPL process,” Abidian said.
They showed that the fabrication process could be performed on glass and flexible substrate poly(dimethylsilosane). They demonstrated that loading as low as 0.5 wt% OS into the resin remarkably increased electrical conductivity of printed organic semiconductor composite polymer over 10 orders of magnitude.
“The excellent electrical conductivity can be attributed to presence of OS in the cross-linked polymer chains, providing both ionic and electronic conduction pathways along the polymer chains,” Abidian said. More

Theoretical scientists have used topological mathematics and machine learning to identify a hidden relationship between nano-scale structures and thermal conductivity in amorphous silicon, a glassy form of the material with no repeating crystalline order.
A study describing their technique appeared in the Journal of Chemical Physics on 23 June.
Amorphous solids, such as glass, obsidian, wax, and plastics, have no long-range repeating, or crystalline structure, to the atoms or molecules that they are made out of. This contrasts with crystalline solids, such as salt, most metals and rocks. As they lack long-range order in their structure, the thermal conductivity of amorphous solids can be far lower than a crystalline solid composed of the same material.
However, there can still be some medium-range order on the scale of nanometers. This medium-range order should affect the propagation and diffusion of atomic vibrations, which carry heat. The heat transport in disordered materials is of special interest to physicists due to its importance in industrial applications. The amorphous form of silicon is used in an enormous range of applications in the modern world, from solar cells to image sensors. For this reason, researchers have intensively investigated the structural signature of the medium-range order in amorphous silicon and how it relates to thermal conductivity.
“For better control over applications that make use of amorphous silicon, controlling its thermal properties is high on engineers’ wish list,” said Emi Minamitani, the corresponding author of the study and a theoretical molecular scientist with the Institute for Molecular Science in Okazaki, Japan. “Extracting the nano-scale structural characteristics in amorphous including medium-range order is an important key.”
Unfortunately, researchers have struggled to carry out this task because it is difficult to determine the essential nano-scale features of disordered systems using traditional techniques. More

Communication networks need nodes at which information is processed or rerouted. Physicists at the University of Basel have now developed a network node for quantum communication networks that can store single photons in a vapor cell and pass them on later.
In quantum communication networks, information is transmitted by single particles of light (photons). At the nodes of such a network buffer elements are needed which can temporarily store, and later re-emit, the quantum information contained in the photons.
Researchers at the University of Basel in the group of Prof. Philipp Treutlein have now developed a quantum memory that is based on an atomic gas inside a glass cell. The atoms do not have to be specially cooled, which makes the memory easy to produce and versatile, even for satellite applications. Moreover, the researchers have realized a single photon source which allowed them to test the quality and storage time of the quantum memory. Their results were recently published in the scientific journal PRX Quantum.
Warm atoms in vapor cells
“The suitability of warm atoms in vapor cells for quantum memories has been investigated for the past twenty years,” says Gianni Buser, who worked on the experiment as a PhD student. “Usually, however, attenuated laser beams — and hence classical light — were used.” In classical light, the number of photons hitting the vapor cell in a certain period follows a statistical distribution; on average it is one photon, but sometimes it can be two, three or none.
To test the quantum memory with “quantum light” — that is, always precisely one photon — Treutlein and his co-workers developed a dedicated single photon source that emits exactly one photon at a time. The instant when that happens is heralded by a second photon, which is always sent out simultaneously with the first one. This allows the quantum memory to be activated at the right moment.
The single photon is then directed into the quantum memory where, with the help of a control laser beam, the photon causes more than a billion rubidium atoms to take on a so-called superposition state of two possible energy levels of the atoms. The photon itself vanishes in the process, but the information contained in it is transformed into the superposition state of the atoms. A brief pulse of the control laser can then read out that information after a certain storage time and transform it back into a photon.
Reducing read-out noise
“Up to now, a critical point has been noise — additional light that is produced during the read-out and that can compromise the quality of the photon,” explains Roberto Mottola, another PhD student in Treutlein’s lab. Using a few tricks, the physicists were able to reduce that noise sufficiently so that after storage times of several hundred nanoseconds the single photon quality was still high.
“Those storage times are not very long, and we didn’t actually optimize them for this study,” Treutlein says, “but already now they are more than a hundred times longer than the duration of the stored single photon pulse.” This means that the quantum memory developed by the Basel researchers can already be employed for interesting applications. For instance, it can synchronize randomly produced single photons, which can then be used in various quantum information applications.
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A new deep learning (DL) model that can identify disease-related features from images of eyes has been unveiled by a group of Tohoku University researchers. This ‘lightweight’ DL model can be trained with a small number of images, even ones with a high-degree of noise, and is resource-efficient, meaning it is deployable on mobile devices.
Details were published in the journal Scientific Reports on May 20, 2022.
With many societies aging and limited medical personnel, DL model reliant self-monitory and tele-screening of diseases are becoming more routine. Yet, deep learning algorithms are generally task specific, and identify or detect general objects such as humans, animals, or road signs.
Identifying diseases, on the other hand, demands precise measurement of tumors, tissue volume, or other sorts of abnormalities. To do so requires a model to look at separate images and mark boundaries in a process known as segmentation. But accurate prediction takes greater computational output, rendering them difficult to deploy on mobile devices.
“There is always a trade-off between accuracy, speed and computational resources when it comes to DL models,” says Toru Nakazawa, co-author of the study and professor at Tohoku University’s Department of Ophthalmology. “Our developed model has better segmentation accuracy and enhanced model training reproducibility, even with fewer parameters — making it efficient and more lightweight when compared to other commercial softwares.”
Professor Nakazawa, Associate Professor Parmanand Sharma, Dr Takahiro Ninomiya, and students from the Department of Ophthalmology worked with professor Takayuki Okatani from Tohoku University’s Graduate School of Information Sciences to produce the model.
Using low resource devices, they obtained measurements of the foveal avascular zone, a region with the fovea centralis at the center of the retina, to enhance screening for glaucoma.
“Our model is also capable of detecting/segmenting optic discs and hemorrhages in fundus images with high precision,” added Nakazawa.
In the future, the group is hopeful of deploying the lightweight model to screen for other common eye disorders and other diseases.
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Researchers created a special ultrathin sensor, spun from gold, that can be attached directly to the skin without irritation or discomfort. The sensor can measure different biomarkers or substances to perform on-body chemical analysis. It works using a technique called Raman spectroscopy, where laser light aimed at the sensor is changed slightly depending on whatever chemicals are present on the skin at that point. The sensor can be finely tuned to be extremely sensitive, and is robust enough for practical use.
Wearable technology is nothing new. Perhaps you or someone you know wears a smartwatch. Many of these can monitor certain health matters such as heart rate, but at present they cannot measure chemical signatures which could be useful for medical diagnosis. Smartwatches or more specialized medical monitors are also relatively bulky and often quite costly. Prompted by such shortfalls, a team comprising researchers from the Department of Chemistry at the University of Tokyo sought a new way to sense various health conditions and environmental matters in a noninvasive and cost-effective manner.
“A few years ago, I came across a fascinating method for producing robust stretchable electronic components from another research group at the University of Tokyo,” said Limei Liu, a visiting scholar at the time of the study and currently a lecturer at Yangzhou University in China. “These devices are spun from ultrafine threads coated with gold, so can be attached to the skin without issue as gold does not react with or irritate the skin in any way. As sensors, they were limited to detecting motion however, and we were looking for something that could sense chemical signatures, biomarkers and drugs. So we built upon this idea and created a noninvasive sensor that exceeded our expectations and inspired us to explore ways to improve its functionality even further.”
The main component of the sensor is the fine gold mesh, as gold is unreactive, meaning that when it comes into contact with a substance the team wishes to measure — for example a potential disease biomarker present in sweat — it does not chemically alter that substance. But instead, as the gold mesh is so fine, it can provide a surprisingly large surface for that biomarker to bind to, and this is where the other components of the sensor come in. As a low-power laser is pointed at the gold mesh, some of the laser light is absorbed and some is reflected. Of the light reflected, most has the same energy as the incoming light. However, some incoming light loses energy to the biomarker or other measurable substance, and the discrepancy in energy between reflected and incident light is unique to the substance in question. A sensor called a spectrometer can use this unique energy fingerprint to identify the substance. This method of chemical identification is known as Raman spectroscopy.
“Currently, our sensors need to be finely tuned to detect specific substances, and we wish to push both the sensitivity and specificity even further in future,” said Assistant Professor Tinghui Xiao. “With this, we think applications like glucose monitoring, ideal for sufferers of diabetes, or even virus detection, might be possible.”
“There is also potential for the sensor to work with other methods of chemical analysis besides Raman spectroscopy, such as electrochemical analysis, but all these ideas require a lot more investigation,” said Professor Keisuke Goda. “In any case, I hope this research can lead to a new generation of low-cost biosensors that can revolutionize health monitoring and reduce the financial burden of health care.”
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