Finding antigens that trigger specific immune cells

The microwells in each experiment, seen in this micrograph by the black outlines, were loaded with spherical beads (the large grey circle in each well), and T cells (the smaller circles adhering to some of the beads).
Image credit: Yinnian Feng

Their approach, which mimics the physical forces exerted by immune cells as they crawl over host cells, could help scientists develop more effective cancer immunotherapies.

A cell’s secrets can be divulged by its surface, decorated with tens to hundreds of thousands of molecules that help immune cells determine friend from foe. Some of those protruding molecules are antigens that trigger the immune system to attack, but it can be difficult for scientists to identify those antigens, which often vary across individuals, in the molecular forest.

A team of Stanford scientists led by Polly Fordyce, an Institute Scholar at Sarafan ChEM-H, has developed a new method to faster and more accurately predict which antigens will lead to a strong immune response. Their approach, which was reported in Nature Methods on Sept. 5, could help scientists develop more effective cancer immunotherapies.

T cells, a class of immune cells, crawl along and squish past other cells as they patrol the body, using T cell receptors to molecularly read peptides, or short pieces of proteins – which are cradled within larger proteins called major histocompatibility complexes (pMHCs) that project from cell surfaces. Healthy host cells display an array of pMHCs that do not trigger an immune response, but once T cells recognize disease-indicating peptides, they become activated to find and kill cells bearing these foreign signatures. Understanding how T cells sensitively distinguish these antigenic peptides from host peptides to avoid mistakenly killing host cells has long been a mystery.

Archaeologists discover monumental evidence of prehistoric hunting across Arabian desert

Distribution of kite structures in the Levant and in northern Arabia. White: previously documented kites. Red: kites recorded by EAMENA.
Credit: EAMENA

Archaeologists at the University of Oxford’s School of Archaeology have used satellite imagery to identify and map over 350 monumental hunting structures known as ‘kites’ across northern Saudi Arabia and southern Iraq – most of which had never been previously documented.

Led by Dr Michael Fradley, a team of researchers in the Endangered Archaeology in the Middle East and North Africa (EAMENA) project used a range of open-source satellite imagery to carefully study the region around the eastern Nafud desert, an area little studied in the past. The surprising results, published in the journal The Holocene, have the potential to change our understanding of prehistoric connections and climate change across the Middle East.

Termed kites by early aircraft pilots, these structures consist of low stone walls making up a head enclosure and a number of guiding walls, sometimes kilometers long. They are believed to have been used to guide game such as gazelles into an area where they could be captured or killed. There is evidence that these structures may date back as far as 8,000 BCE in the Neolithic period.

Kites cannot be observed easily from the ground, however the advent of commercial satellite imagery and platforms such as Google Earth have enabled recent discoveries of new distributions. While these structures were already well-known from eastern Jordan and adjoining areas in southern Syria, these latest results take the known distribution over 400km further east across northern Saudi Arabia, with some also identified in southern Iraq for the first time.

Scientists Found Luminescence in Unique Dielectric Material

Hafnium dioxide was studied in the Ural Federal University Research Center Nanomaterials and Nanotechnologies.
Photo credit: Ilya Safarov

Scientists from the UrFU studied the luminescent properties of hafnium dioxide, a material with high dielectric permittivity. This compound is used in the micro- and nanoelectronics industry. Physicists found that due to the presence of exotic quasiparticles in their electronic structure, the compound exhibits intense luminescence at extremely low temperatures. The discovery will help in the creation of future electronic devices, such as lasers, optical sensors, or biomedical sensors. The results are presented in the Journal of Luminescence.

"We studied the temperature effect on the luminescence properties of the nanostructured hafnium dioxide powder. When we cooled the compound to 40 K (-233°C), we recorded ultraviolet luminescence in the compound, which became brighter as the sample cooled. We were able to build a model that describes at what point in the compound additional luminescence is formed, how the intensity of the luminescence changes and is characterized. This model can be useful in the development of highly sensitive sensors in modern optoelectronic devices or compact biosensors for visualization of various processes," notes Artyom Shilov, Junior Researcher at the Nanotech Research Center at UrFU Ural Interregional Research and Scientific Center.

A Novel Approach to Creating Tailored Odors and Fragrances Using Machine Learning

Can we use machine learning methods to predict the sensing data of odor mixtures and design new smells? A new study by researchers from Tokyo Tech does just that. The novel method is bound to have applications in the food, health, beauty, and wellness industries, where odors and fragrances are of keen interest.

The sense of smell is one of the basic senses of animal species. It is critical to finding food, realizing attraction, and sensing danger. Humans detect smells, or odorants, with olfactory receptors expressed in olfactory nerve cells. These olfactory impressions of odorants on nerve cells are associated with their molecular features and physicochemical properties. This makes it possible to tailor odors to create an intended odor impression. Current methods only predict olfactory impressions from the physicochemical features of odorants. But that method cannot predict the sensing data, which is indispensable for creating smells.

To tackle this issue, scientists from Tokyo Institute of Technology (Tokyo Tech) have employed the innovative strategy of solving the inverse problem. Instead of predicting the smell from molecular data, this method predicts molecular features based on the odor impression. This is achieved using standard mass spectrum data and machine learning (ML) models. "We used a machine-learning-based odor predictive model that we had previously developed to obtain the odor impression. Then we predicted the mass spectrum from odor impression inversely based on the previously developed forward model," explains Professor Takamichi Nakamoto, the leader of the research effort by Tokyo Tech. The findings have been published in PLoS One.

Researchers devise tunable conducting edge

In their experiments, the researchers stacked monolayer WTe2 with Cr2Ge2Te6, or
CGT. Credit: Shi lab/UC Riverside

A research team led by a physicist at the University of California, Riverside, has demonstrated a new magnetized state in a monolayer of tungsten ditelluride, or WTe2, a new quantum material. Called a magnetized or ferromagnetic quantum spin Hall insulator, this material of one-atom thickness has an insulating interior but a conducting edge, which has important implications for controlling electron flow in nanodevices.

In a typical conductor, electrical current flows evenly everywhere. Insulators, on the other hand, do not readily conduct electricity. Ordinarily, monolayer WTe2 is a special insulator with a conducting edge; magnetizing bestows upon it more unusual properties.

“We stacked monolayer WTe2 with an insulating ferromagnet of several atomic layer thickness — of Cr2Ge2Te6, or simply CGT — and found that the WTe2 had developed ferromagnetism with a conducting edge,” said Jing Shi, a distinguished professor of physics and astronomy at UCR, who led the study. “The edge flow of the electrons is unidirectional and can be made to switch directions with the use of an external magnetic field.”

Shi explained that when only the edge conducts electricity, the size of the interior of the material is inconsequential, allowing electronic devices that use such materials to be made smaller — indeed, nearly as small as the conducting edge. Because devices using this material would consume less power and dissipate less energy, they could be made more energy efficient. Batteries using this technology, for example, would last longer.

Bronchodilators don’t improve smoking-related respiratory symptoms in people without COPD

Credit: Ralph from Pixabay

Researchers supported by the National Institutes of Health have found that dual bronchodilators – long-lasting inhalers that relax the airways and make it easier to breathe – do little to help people who do not have chronic obstructive pulmonary disease (COPD), but who do have respiratory symptoms and a history of smoking.

COPD, a lung disease that obstructs the airways and leads to coughing, wheezing, and shortness of breath, affects about 15 million Americans. However, millions of others who smoke or used to smoke and have some symptoms of COPD have also been prescribed bronchodilators.

“We’ve assumed these medications worked in patients who don’t meet lung function criteria for COPD, but we never checked,” said MeiLan K. Han, M.D., a principal investigator and first author of the study. “We now know these existing medications don’t work for these patients.”

The findings of the study, which was funded by the National Heart, Lung, and Blood Institute (NHLBI), were published in the New England Journal of Medicine and simultaneously presented at the European Respiratory Society International Congress.

According to scientists, the implications are significant. First, they show the importance of diagnosing lung conditions through spirometry, a lung function test Han noted is underutilized in clinical practice. Second, they show the need for new, effective therapies for patients without COPD.

New treatment could result in more donor lungs

Sandra Lindstedt, Snejana Hyllén, and Leif Pierre
Credit: Lund University

A large number of lungs donated cannot be used for transplantation. Researchers at Lund University in Sweden and Skåne University Hospital have conducted an animal study bringing hope that more donor lungs could be used in the future. Researchers have launched a pilot study to investigate whether the treatment will have the same positive effects on human beings.

About 190 organs are donated in Sweden every year. Due to injuries to the lungs, only about 30 percent of them can be used for transplantation. Adding to that, the mortality rate is high: about half of the patients pass away within five years of transplant.

” The results from our study indicate that a certain treatment can help us use a larger part of a donor lung, and that there is an improved outcome during the first two days after surgery”, says Sandra Lindstedt, senior consultant in thoracic surgery at Skåne University Hospital and adjunct professor at Lund University.

In their study on pigs, the researchers investigated the effects of reducing the levels of cytokines in lungs. Cytokines are small proteins that are produced by specific cells of the immune system.

High plant diversity is often found in the smallest of areas

This meadow in Romania is one of the most species-rich regions on earth - in 2009, a research team found 98 plant species here.
Credit: Jürgen Dengler

It might sound weird, but it's true: the steppes of Eastern Europe are home to a similar number of plant species as the regions of the Amazon rainforest. However, this is only apparent when species are counted in small sampling areas, rather than hectares of land. An international team of researchers led by the Martin Luther University Halle-Wittenberg (MLU) and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig has now shown how much estimates of plant diversity change when the sampling area ranges from a few square meters to hectares. Their results have been published in the journal "Nature Communications" and could be utilized in new, more tailored nature conservation concepts.

In their study, the team analyzed a dataset of around 170,000 vegetation plots from all of the Earth’s climate zones. The data included information on all of the plant species found at a location and the coordinates of the respective area under study. The data was taken from the globally unique vegetation database "sPlot", which is located at iDiv.

"Most studies on global biodiversity are conducted on a relatively large scale, for example at a state or provincial scale. We wanted to find out how much results differ when smaller areas are examined," says Professor Helge Bruelheide from MLU. The team used artificial intelligence to investigate, among other things, the relationship between the number of plant species and the size of the area under study.

Boy’s discovery reveals highly complex plant-insect interaction

Researchers discovered that not only do gall wasps manipulate oaks to produce galls, but they also manipulate ants to retrieve the galls to their nests, where the wasp larvae may be protected from gall predators or receive other benefits. 

Credit: Michael Tribone, Penn State.

When 8-year-old Hugo Deans discovered a handful of BB-sized objects lying near an ant nest beneath a log in his backyard, he thought they were a type of seed. His father, Andrew Deans, professor of entomology at Penn State, however, knew immediately what they were — oak galls, or plant growths triggered by insects. What he didn’t realize right away was that the galls were part of an elaborate relationship among ants, wasps and oak trees, the discovery of which would turn a century of knowledge about plant-insect interactions on its head.

Looking back, Hugo, now 10, said, “I thought they were seeds, and I felt excited because I didn’t know ants collected seeds. I always thought ants would eat food scraps and stuff around the house. Then I got more excited when [my dad] told me they were galls, because [my dad] was so excited. I was surprised that ants would collect galls because why would they do that?”

According to Andrew Deans, who is also the director of Penn State’s Frost Entomological Museum, many plant-insect interactions are well documented. For example, most "cynipid" wasp species have long been known to induce oak trees to produce protective galls — or growths — around their larvae to ensure the safety of their developing offspring. Additionally, certain plants — including bloodroot (Sanguinaria canadensis), a wildflower native to North America — produce edible appendages, called elaiosomes, on their seeds to attract ants, which then disperse the seeds by carrying them back to their nests. This latter example is referred to as "myrmecochory" — or seed dispersal by ants.

How Artificial Intelligence can explain its decisions

They have brought together the seemingly incompatible inductive approach of machine learning with deductive logic: Stephanie Schörner, Axel Mosig and David Schuhmacher (from left).
Credit: RUB, Marquard

If an algorithm in a tissue sample makes up a tumor, it does not yet reveal how it came to this result. It is not very trustworthy. Bochum researchers are therefore taking a new approach.

Artificial intelligence (AI) can be trained to recognize whether a tissue image contains tumor. How she makes her decision has so far remained hidden. A team from the Research Center for Protein Diagnostics, or PRODI for short, at the Ruhr University Bochum is developing a new approach: with it, the decision of an AI can be explained and thus trustworthy. The researchers around Prof. Dr. Axel Mosig in the journal "Medical Image Analysis".

Bioinformatician Axel Mosig cooperated with Prof. Dr. Andrea Tannapfel, head of the Institute of Pathology, the oncologist Prof. Dr. Anke Reinacher-Schick from St. Josef Hospital of the Ruhr University as well as the biophysicist and PRODI founding director Prof. Dr. Klaus Gerwert. The group developed a neural network, i.e. an AI that can classify whether a tissue sample contains tumor or not. To do this, they fed the AI with many microscopic tissue images, some of which contained tumors, others were tumor-free.