Aurelia Butler

It takes a lot of time — and money — to diagnose Alzheimer’s disease. After running lengthy in-person neuropsychological exams, clinicians have to transcribe, review, and analyze every response in detail. But researchers at Boston University have developed a new tool that could automate the process and eventually allow it to move online. Their machine learning-powered computational model can detect cognitive impairment from audio recordings of neuropsychological tests — no in-person appointment needed. Their findings were published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association.
“This approach brings us one step closer to early intervention,” says Ioannis Paschalidis, a coauthor on the paper and a BU College of Engineering Distinguished Professor of Engineering. He says faster and earlier detection of Alzheimer’s could drive larger clinical trials that focus on individuals in early stages of the disease and potentially enable clinical interventions that slow cognitive decline: “It can form the basis of an online tool that could reach everyone and could increase the number of people who get screened early.”
The research team trained their model using audio recordings of neuropsychological interviews from over 1,000 individuals in the Framingham Heart Study, a long-running BU-led project looking at cardiovascular disease and other physiological conditions. Using automated online speech recognition tools — think, “Hey, Google!” — and a machine learning technique called natural language processing that helps computers understand text, they had their program transcribe the interviews, then encode them into numbers. A final model was trained to assess the likelihood and severity of an individual’s cognitive impairment using demographic data, the text encodings, and real diagnoses from neurologists and neuropsychologists.
Paschalidis says the model was not only able to accurately distinguish between healthy individuals and those with dementia, but also detect differences between those with mild cognitive impairment and dementia. And, it turned out, the quality of the recordings and how people spoke — whether their speech breezed along or consistently faltered — were less important than the content of what they were saying.
“It surprised us that speech flow or other audio features are not that critical; you can automatically transcribe interviews reasonably well, and rely on text analysis through AI to assess cognitive impairment,” says Paschalidis, who’s also the new director of BU’s Rafik B. Hariri Institute for Computing and Computational Science & Engineering. Though the team still needs to validate its results against other sources of data, the findings suggest their tool could support clinicians in diagnosing cognitive impairment using audio recordings, including those from virtual or telehealth appointments.
Screening before Symptom Onset
The model also provides insight into what parts of the neuropsychological exam might be more important than others in determining whether an individual has impaired cognition. The researchers’ model splits the exam transcripts into different sections based on the clinical tests performed. They discovered, for instance, that the Boston Naming Test — during which clinicians ask individuals to label a picture using one word — is most informative for an accurate dementia diagnosis. “This might enable clinicians to allocate resources in a way that allows them to do more screening, even before symptom onset,” says Paschalidis.
Early diagnosis of dementia is not only important for patients and their caregivers to be able to create an effective plan for treatment and support, but it’s also crucial for researchers working on therapies to slow and prevent Alzheimer’s disease progression. “Our models can help clinicians assess patients in terms of their chances of cognitive decline,” says Paschalidis, “and then best tailor resources to them by doing further testing on those that have a higher likelihood of dementia.”
Want to Join the Research Effort?
The research team is looking for volunteers to take an online survey and submit an anonymous cognitive test — results will be used to provide personalized cognitive assessments and will also help the team refine their AI model.
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Materials provided by Boston University. Original written by Gina Mantica. Note: Content may be edited for style and length. More

Frequent players of video games show superior sensorimotor decision-making skills and enhanced activity in key regions of the brain as compared to non-players, according to a recent study by Georgia State University researchers.
The authors, who used functional magnetic resonance imaging (FMRI) in the study, said the findings suggest that video games could be a useful tool for training in perceptual decision-making.
“Video games are played by the overwhelming majority of our youth more than three hours every week, but the beneficial effects on decision-making abilities and the brain are not exactly known,” said lead researcher Mukesh Dhamala, associate professor in Georgia State’s Department of Physics and Astronomy and the university’s Neuroscience Institute.
“Our work provides some answers on that,” Dhamala said. “Video game playing can effectively be used for training — for example, decision-making efficiency training and therapeutic interventions — once the relevant brain networks are identified.”
Dhamala was the adviser for Tim Jordan, the lead author of the paper, who offered a personal example of how such research could inform the use of video games for training the brain.
Jordan, who received a Ph.D. in physics and astronomy from Georgia State in 2021, had weak vision in one eye as a child. As part of a research study when he was about 5, he was asked to cover his good eye and play video games as a way to strengthen the vision in the weak one. Jordan credits video game training with helping him go from legally blind in one eye to building strong capacity for visual processing, allowing him to eventually play lacrosse and paintball. He is now a postdoctoral researcher at UCLA. More

Machine learning is transforming all areas of biological science and industry, but is typically limited to a few users and scenarios. A team of researchers at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has developed METIS, a modular software system for optimizing biological systems. The research team demonstrates its usability and versatility with a variety of biological examples.
Though engineering of biological systems is truly indispensable in biotechnology and synthetic biology, today machine learning has become useful in all fields of biology. However, it is obvious that application and improvement of algorithms, computational procedures made of lists of instructions, is not easily accessible. Not only are they limited by programming skills but often also insufficient experimentally-labeled data. At the intersection of computational and experimental works, there is a need for efficient approaches to bridge the gap between machine learning algorithms and their applications for biological systems.
Now a team at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has succeeded in democratizing machine learning. In their recent publication in “Nature Communications,” the team presented together with collaboration partners from the INRAe Institute in Paris, their tool METIS. The application is built in such a versatile and modular architecture that it does not require computational skills and can be applied on different biological systems and with different lab equipment. METIS is short from Machine-learning guided Experimental Trials for Improvement of Systems and also named after the ancient goddess of wisdom and crafts Μῆτις, lit. “wise counsel.”
Less data required
Active learning, also known as optimal experimental design, uses machine learning algorithms to interactively suggest the next set of experiments after being trained on previous results, a valuable approach for wet-lab scientists, especially when working with a limited number of experimentally-labeled data. But one of the main bottlenecks is the experimentally-labeled data generated in the lab that are not always high enough to train machine learning models. “While active learning already reduces the need for experimental data, we went further and examined various machine learning algorithms. Encouragingly, we found a model that is even less dependent on data,” says Amir Pandi, one of the lead authors of the study.
To show the versatility of METIS, the team used it for a variety of applications, including optimization of protein production, genetic constructs, combinatorial engineering of the enzyme activity, and a complex CO2 fixation metabolic cycle named CETCH. For the CETCH cycle, they explored a combinatorial space of 1025 conditions with only 1,000 experimental conditions and reported the most efficient CO2 fixation cascade described to date.
Optimizing biological systems
In application, the study provides novel tools to democratize and advance current efforts in biotechnology, synthetic biology, genetic circuit design, and metabolic engineering. “METIS allows researchers to either optimize their already discovered or synthesized biological systems,” says Christoph Diehl, Co-lead author of the study. “But it is also a combinatorial guide for understanding complex interactions and hypothesis-driven optimization. And what is probably the most exciting benefit: it can be a very helpful system for prototyping new-to-nature systems.”
METIS is a modular tool running as Google Colab Python notebooks and can be used via a personal copy of the notebook on a web browser, without installation, registration, or the need for local computational power. The materials provided in this work can guide users to customize METIS for their applications.
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Materials provided by Max-Planck-Gesellschaft. Note: Content may be edited for style and length. More

As our devices get smaller and smaller, the use of molecules as the main components in electronic circuitry is becoming ever more critical. Over the past 10 years, researchers have been trying to use single molecules as conducting wires because of their small scale, distinct electronic characteristics, and high tunability. But in most molecular wires, as the length of the wire increases, the efficiency by which electrons are transmitted across the wire decreases exponentially.This limitation has made it especially challenging to build a long molecular wire — one that is much longer than a nanometer — that actually conducts electricity well.
Columbia researchers announced today that they have built a nanowire that is 2.6 nanometers long, shows an unusual increase in conductance as the wire length increases, and has quasi-metallic properties. Its excellent conductivity holds great promise for the field of molecular electronics, enabling electronic devices to become even tinier. The study is published today in Nature Chemistry.
Molecular wire designs
The team of researchers from Columbia Engineering and Columbia’s department of chemistry, together with theorists from Germany and synthetic chemists in China, explored molecular wire designs that would support unpaired electrons on either end, as such wires would form one-dimensional analogues to topological insulators (TI) that are highly conducting through their edges but insulating in the center.
While the simplest 1D TI is made of just carbon atoms where the terminal carbons support the radical states — unpaired electrons, these molecules are generally very unstable. Carbon does not like to have unpaired electrons. Replacing the terminal carbons, where the radicals are, with nitrogen increases the molecules’ stability. “This makes 1D TIs made with carbon chains but terminated with nitrogen much more stable and we can work with these at room temperature under ambient conditions,” said the team’s co-leader Latha Venkataraman, Lawrence Gussman Professor of Applied Physics and professor of chemistry.
Breaking the exponential-decay rule
Through a combination of chemical design and experiments, the group created a series of one-dimensional TIs and successfully broke the exponential-decay rule, a formula for the process of a quantity decreasing at a rate proportional to its current value. Using the two radical-edge states, the researchers generated a highly conducting pathway through the molecules and achieved a “reversed conductance decay,” i.e. a system that shows an increasing conductance with increasing wire length.
“What’s really exciting is that our wire had a conductance at the same scale as that of a gold metal-metal point contacts, suggesting that the molecule itself shows quasi-metallic properties,” Venkataraman said. “This work demonstrates that organic molecules can behave like metals at the single-molecule level in contrast to what had been done in the past where they were primarily weakly conducting.”
The researchers designed and synthesized a bis(triarylamines) molecular series, which exhibited properties of a one-dimensional TI by chemical oxidation. They made conductance measurements of single-molecule junctions where molecules were connected to both the source and drain electrodes. Through the measurements, the team showed that the longer molecules had a higher conductance, which worked until the wire was longer than 2.5 nanometers, the diameter of a strand of human DNA.
Laying the groundwork for more technological advancements in molecular electronics
“The Venkataraman lab is always seeking to understand the interplay of physics, chemistry, and engineering of single-molecule electronic devices,” added Liang Li, a PhD student in the lab, and a co-first author of the paper. “So creating these particular wires will lay the groundwork for major scientific advances in understanding transport through these novel systems. We’re very excited about our findings because they shed light not only on fundamental physics, but also on potential applications in the future.”
The group is currently developing new designs to build molecular wires that are even longer and still highly conductive.
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Materials provided by Columbia University School of Engineering and Applied Science. Original written by Holly Evarts. Note: Content may be edited for style and length. More

Researchers from LMU and Saarland University have entangled two quantum memories over a 33-kilometer-long fiber optic connection — a record and an important step toward the quantum internet.
A network in which data transmission is perfectly secure against hacking? If physicists have their way, this will one day become a reality with the help of the quantum mechanical phenomenon known as entanglement. For entangled particles, the rule is: If you measure the state of one of the particles, then you automatically know the state of the other. It makes no difference how far away the entangled particles are from each other. This is an ideal state of affairs for transmitting information over long distances in a way that renders eavesdropping impossible.
A team led by physicists Prof. Harald Weinfurter from LMU and Prof. Christoph Becher from Saarland University have now coupled two atomic quantum memories over a 33-kilometer-long fiber optic connection. This is the longest distance so far that anyone has ever managed entanglement via a telecom fiber. The quantum mechanical entanglement is mediated via photons emitted by the two quantum memories. A decisive step was the researchers’ shifting of the wavelength of the emitted light particles to a value that is used for conventional telecommunications. “By doing this, we were able to significantly reduce the loss of photons and create entangled quantum memories even over long distances of fiber optic cable,” says Weinfurter.
Generally speaking, quantum networks consist of nodes of individual quantum memories — such as atoms, ions, or defects in crystal lattices. These nodes are able to receive, store, and transmit quantum states. Mediation between the nodes can be accomplished using light particles that are exchanged either over the air or in a targeted manner via fiber optic connection. For their experiment, the researchers use a system comprised of two optically trapped rubidium atoms in two laboratories on the LMU campus. The two locations are connected via a 700-meter-long fiber optic cable, which runs underneath Geschwister Scholl Square in front of the main building of the university. By adding extra fibers on coils, connections of up to 33 kilometers in length can be achieved.
A laser pulse excites the atoms, after which they spontaneously fall back into their ground state, each thereby emitting a photon. Due to the conservation of angular momentum, the spin of the atom is entangled with the polarization of its emitted photon. These light particles can then be used to create a quantum mechanical coupling of the two atoms. To do this, the scientists sent them through the fiber optic cable to a receiver station, where a joint measurement of the photons indicates an entanglement of the quantum memories.
However, most quantum memories emit light with wavelengths in the visible or near-infrared range. “In fiber optics, these photons make it just a few kilometers before they are lost,” explains Christoph Becher. For this reason, the physicist from Saarbrücken and his team optimized the wavelength of the photons for their journey in the cable. Using two quantum frequency converters, they increased the original wavelength from 780 nanometers to a wavelength of 1,517 nanometers. “This is close to the so-called telecom wavelength of around 1,550 nanometers,” says Becher. The telecom band is the frequency range in which the transmission of light in fiber optics has the lowest losses. Becher’s team accomplished the conversion with an unprecedented efficiency of 57 percent. At the same time, they managed to preserve the quality of the information stored in the photons to a high degree, which is a condition of quantum coupling.
“The significance of our experiment is that we actually entangle two stationary particles — that is to say, atoms that function as quantum memories,” says Tim van Leent, lead author of the paper. “This is much more difficult than entangling photons, but it opens up many more application possibilities.” The researchers think that the system they developed could be used to construct large-scale quantum networks and for the implementation of secure quantum communication protocols. “The experiment is an important step on the path to the quantum internet based on existing fiber optic infrastructure,” says Harald Weinfurter.
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In materials science, candidates for novel functional materials are usually explored in a trial-and-error fashion through calculations, synthetic methods, and material analysis. However, the approach is time-consuming and requires expertise. Now, researchers from Japan have used a data-driven approach to automate the process of predicting new magnetic materials. By combining first-principles calculations, Bayesian optimization, and monoatomic alternating deposition, the proposed method can enable a faster development of next-generation electronic devices.
Materials scientists are constantly on the lookout for new “functional materials” with favorable properties directed towards some application. For instance, finding novel functional magnetic materials could open doors to energy-efficient spintronic devices. In recent years, the development of spintronics devices like magnetoresistive random access memory — an electronic device in which a single magnetoresistive element is integrated as one bit of information — has been progressing rapidly, for which magnetic materials with high magnetocrystalline anisotropy (MCA) are required. Ferromagnetic materials, which retain their magnetization without an external magnetic field, are of particular interest as data storage systems, therefore. For instance, L10-type ordered alloys consisting of two elements and two periods, such as L10-FeCo and L10-FeNi, have been studied actively as promising candidates for next-generation functional magnetic materials. However, the combination of constituent elements is extremely limited, and materials with extended element type, number, and periodicity have rarely been explored.
What impedes this exploration? Scientists point at combinatorial explosions that can occur easily in multilayered films, requiring a great deal of time and effort in the selection of the constituent elements and material fabrication, as the major reason. Besides, it is extremely difficult to predict the function of MCA because of the complex interplay of various parameters including crystal structure, magnetic moment, and electronic state, and the conventional protocol relies largely on trial and error. Thus, there is much scope and need for developing an efficient route to discovering new high-performance magnetic materials.
On this front, a team of researchers from Japan including Prof. Masato Kotsugi, Mr. Daigo Furuya, and Mr. Takuya Miyashita from Tokyo University of Science (TUS), along with Dr. Yoshio Miura from the National Institute for Materials Science (NIMS), has now turned to a data-driven approach for automating the prediction and synthesis of new magnetic materials. In a new study, which was made available online on June 30, 2022 and published in Science and Technology of Advanced Materials: Methods on July 1, 2022, the team reported their success in the development of material exploration system by integrating computational, information, and experimental sciences for high MCA magnetic materials. Prof. Kotsugi explains, “We have focused on artificial intelligence and have combined it with computational and experimental science to develop an efficient material synthesis method. Promising materials beyond human expectation have been discovered in terms of electronic structure. Thus, it will change the nature of materials engineering!”
In their study, which was the result of joint research by TUS and NIMS and supported by JST-CREST, the team calculated MCA energy through first-principles calculations (a method used to calculate electronic states and physical properties in materials based on the laws of quantum mechanics) and performed Bayesian optimization to search for materials with high MCA energy. After examining the algorithm for Bayesian optimization, they found promising materials five times more efficiently than through the conventional trial-and-error approach. This robust material search method was less susceptible to influences from irregular factors like outliers and noise and allowed the team to select the top three candidate materials — (Fe/Cu/Fe/Cu), (Fe/Cu/Co/Cu), and (Fe/Co/Fe/Ni) — comprising iron (Fe), cobalt (Co), nickel (Ni), and copper (Cu).
The top three predicted materials with the largest MCA energy values were then fabricated via the monoatomic alternating stacking method using the laser-driven pulsed deposition technique to create multilayered magnetic materials consisting of 52 layers, namely [Fe/Cu/Fe/Cu]13, [Fe/Cu/Co/Cu]13, and [Fe/Co/Fe/Ni]13. Among the three structures, [Fe/Co/Fe/Ni]1 showed an MCA value (3.74 × 106 erg/cc) much above that of L10-FeNi (1.30 × 106 erg/cc).
Furthermore, using the second-order perturbation method, the team found that MCA is generated in the electronic state, which has not been realized in previously reported materials. This attests to the suitability of employing Bayesian optimization to identify electronic states that are likely impossible to envision through human experience and intuition alone. Thus, the developed method can autonomously search for suitable elements to design functional magnetic materials. “This technique is extendable to advanced magnetic materials with more complicated electronic correlations, such as Heusler alloys and spin-thermoelectric materials,” observes Prof. Kotsugi. More

When robots appear to engage with people and display human-like emotions, people may perceive them as capable of “thinking,” or acting on their own beliefs and desires rather than their programs, according to research published by the American Psychological Association.
“The relationship between anthropomorphic shape, human-like behavior and the tendency to attribute independent thought and intentional behavior to robots is yet to be understood,” said study author Agnieszka Wykowska, PhD, a principal investigator at the Italian Institute of Technology. “As artificial intelligence increasingly becomes a part of our lives, it is important to understand how interacting with a robot that displays human-like behaviors might induce higher likelihood of attribution of intentional agency to the robot.”
The research was published in the journal Technology, Mind, and Behavior.
Across three experiments involving 119 participants, researchers examined how individuals would perceive a human-like robot, the iCub, after socializing with it and watching videos together. Before and after interacting with the robot, participants completed a questionnaire that showed them pictures of the robot in different situations and asked them to choose whether the robot’s motivation in each situation was mechanical or intentional. For example, participants viewed three photos depicting the robot selecting a tool and then chose whether the robot “grasped the closest object” or “was fascinated by tool use.”
In the first two experiments, the researchers remotely controlled iCub’s actions so it would behave gregariously, greeting participants, introducing itself and asking for the participants’ names. Cameras in the robot’s eyes were also able to recognize participants’ faces and maintain eye contact. The participants then watched three short documentary videos with the robot, which was programmed to respond to the videos with sounds and facial expressions of sadness, awe or happiness.
In the third experiment, the researchers programmed iCub to behave more like a machine while it watched videos with the participants. The cameras in the robot’s eyes were deactivated so it could not maintain eye contact and it only spoke recorded sentences to the participants about the calibration process it was undergoing. All emotional reactions to the videos were replaced with a “beep” and repetitive movements of its torso, head and neck.
The researchers found that participants who watched videos with the human-like robot were more likely to rate the robot’s actions as intentional, rather than programmed, while those who only interacted with the machine-like robot were not. This shows that mere exposure to a human-like robot is not enough to make people believe it is capable of thoughts and emotions. It is human-like behavior that might be crucial for being perceived as an intentional agent.
According to Wykowska, these findings show that people might be more likely to believe artificial intelligence is capable of independent thought when it creates the impression that it can behave just like humans. This could inform the design of social robots of the future, she said.
“Social bonding with robots might be beneficial in some contexts, like with socially assistive robots. For example, in elderly care, social bonding with robots might induce a higher degree of compliance with respect to following recommendations regarding taking medication,” Wykowska said. “Determining contexts in which social bonding and attribution of intentionality is beneficial for the well-being of humans is the next step of research in this area.”
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Materials provided by American Psychological Association. Note: Content may be edited for style and length. More

In 2004, researchers from the University of Manchester used adhesive tape to pull sheets of single carbon atoms away from graphite to make graphene — a material that is 1000 times thinner than human hair yet stronger than steel. This ground-breaking exfoliation technique paved the way for the development of a wide range of two-dimensional materials with distinct electrical and physical characteristics for the next generation of electronic devices.
One such material of interest has been muscovite mica (MuM). These minerals have the general formula KAl2(AlSi3O10) (F, OH)2 and have a layered structure consisting of aluminum (Al), potassium (K), and silicon (Si). Like graphene, MuM has gained attention as an ultra-flat substrate for building flexible electronic devices. Unlike graphene, however, MuM is an insulator.
However, the electrical properties of MuM are not altogether clear. In particular, the properties of single and few-molecule-layer thick MuMs are not clearly understood. This is because in all the studies that have probed the electrical properties of MuM so far, the conductivity has been dominated by a quantum phenomenon called “tunneling.” This has made it difficult to understand the conductive nature of thin MuM.
In a recent study published in the journal Physical Review Applied, Professor Muralidhar Miryala from Shibaura Institute of Technology (SIT), Japan, along with Professors M. S. Ramachandra Rao, Ananth Krishnan and Mr. Ankit Arora, a PhD student, from Indian Institute of Technology Madras, India, have now observed a semiconducting behavior in thin MuM flakes, characterized by an electrical conductivity that is 1000 times larger than that of thick MuM. “Mica has been one of the most popular electrical insulators used in industries for decades. However, this semiconductor-like behavior has not been reported earlier,” says Prof. Miryala.
In their study, the researchers exfoliated thin MuM flakes of varying thickness onto silicon (SiO2/Si) substrates and, to avoid tunneling, maintained a separation of 1 µm between the contact electrodes. On measuring the electrical conductivity, they noticed that the transition to a conducting state occurred gradually as the flakes were thinned down to fewer layers. They found that for MuM flakes below 20 nm, the current depended on the thickness, becoming 1000 times larger for a 10 nm thick MuM (5 layers thick) compared to that in 20 nm MuM.
To make sense of this result, the researchers fitted the experimental conductivity data to a theoretical model called the “hopping conduction model,” which suggested that the observed conductance is due to an increase in the conduction band carrier density with the reduction in thickness. Put simply, as the thickness of MuM flakes is reduced, the energy required to move electrons from the solid bulk to the surface decreases, allowing the electrons easier passage into the “conduction band,” where they can freely move to conduct electricity. As to why the carrier density increases, the researchers attributed it to the effects of surface doping (impurity addition) contributions from K+ ions and relaxation of the MuM crystal structure.
The significance of this finding is that thin exfoliated sheets of MuM have a band structure similar to that of wide bandgap semiconductors. This, combined with its exceptional chemical stability, makes thin MuM flakes an ideal material for two-dimensional electronic devices that are both flexible and durable. “MuM is known for its exceptional stability in harsh environments such as those characterized by high temperatures, pressures, and electrical stress. The semiconductor-like behavior observed in our study indicates that MuM has the potential to pave the way for the development of robust electronics,” says Prof. Miryala.
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