Aurelia Butler

Most deaths from melanoma — the most lethal form of skin cancer — occur in patients who were initially diagnosed with early-stage melanoma and then later experienced a recurrence that is typically not detected until it has spread or metastasized.
A team led by investigators at Massachusetts General Hospital (MGH) recently developed an artificial intelligence-based method to predict which patients are most likely to experience a recurrence and are therefore expected to benefit from aggressive treatment. The method was validated in a study published in npj Precision Oncology.
Most patients with early-stage melanoma are treated with surgery to remove cancerous cells, but patients with more advanced cancer often receive immune checkpoint inhibitors, which effectively strengthen the immune response against tumor cells but also carry significant side effects.
“There is an urgent need to develop predictive tools to assist in the selection of high-risk patients for whom the benefits of immune checkpoint inhibitors would justify the high rate of morbid and potentially fatal immunologic adverse events observed with this therapeutic class,” says senior author Yevgeniy R. Semenov, MD, an investigator in the Department of Dermatology at MGH.
“Reliable prediction of melanoma recurrence can enable more precise treatment selection for immunotherapy, reduce progression to metastatic disease and improve melanoma survival while minimizing exposure to treatment toxicities.”
To help achieve this, Semenov and his colleagues assessed the effectiveness of algorithms based on machine learning, a branch of artificial intelligence, that used data from patient electronic health records to predict melanoma recurrence.
Specifically, the team collected 1,720 early-stage melanomas — 1,172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI) — and extracted 36 clinical and pathologic features of these cancers from electronic health records to predict patients’ recurrence risk with machine learning algorithms. Algorithms were developed and validated with various MGB and DFCI patient sets, and tumor thickness and rate of cancer cell division were identified as the most predictive features.
“Our comprehensive risk prediction platform using novel machine learning approaches to determine the risk of early-stage melanoma recurrence reached high levels of classification and time to event prediction accuracy,” says Semenov. “Our results suggest that machine learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients who may benefit from adjuvant immunotherapy.”
Additional Mass General co-authors include Ahmad Rajeh, Michael R. Collier, Min Seok Choi, Munachimso Amadife, Kimberly Tang, Shijia Zhang, Jordan Phillips, Nora A. Alexander, Yining Hua, Wenxin Chen, Diane, Ho, Stacey Duey, and Genevieve M. Boland.
This work was supported by the Melanoma Research Alliance, the National Institutes of Health, the Department of Defense, and the Dermatology Foundation.
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Scientists at the Max Planck Institute for the Science of Light have managed for the first time to create a unidirectional device that significantly increases the quality of a special class of transmitted signals in optical communications: optical vortices. By transmitting selective optical vortex modes exclusively unidirectionally, the developed device largely reduces detrimental backscattering to a minimum. The scientists emphasize the great practical utility of their discovery in many optical systems, with applications ranging from mode division multiplexed communications, optical tweezers, vortex lasers to quantum manipulation systems.
Optical communication can be improved by increasing the amount of optical information transmitted. This can be achieved by using multiplexed channels such as using many optical wavelengths, different polarization states or multiple time slots. In the last decade, optical spatial modes, which are the eigenfields in the waveguides, are widely exploited to further improve the communication capacity due to the little crosstalk between orthogonal spatial modes.
In classical communication as well as in quantum communication, the use of vortex modes in multiplexing methods has proven to be advantageous. This special mode set possesses a helical optical phase distribution and allows an additional degree of freedom for multiplexing optical signals. Devices like vortex generators, lasers and signal amplifiers were demon-strated and are in great demand.
A limiting effect on the applicability is that there has not yet been a device that permits transmission of certain vortex modes in one direction but not the opposite one. However, just this kind of device — a so-called optical vortex isolator — is of crucial importance for the improvement of signal quality and purity. The particular difficulty in developing such a device is a fundamental principle of optics: reciprocity. It requires a symmetrical response of a transmission channel when the source and observation points are interchanged.
Researchers succeed in building an optical vortex insulator
Now, a team at the Max Planck Institute for the Science of Light led by Xinglin Zeng, Philip Russell and Birgit Stiller, achieved a breakthrough that makes this possible: They used sound waves that propagate only in one direction to break the light transmission reciprocity for chosen vortex modes. The effect of so-called topology-selective Brillouin-Mandelstam scattering in chiral photonic crystal fibre allows for a unidirectional interaction of vortex-carrying light waves with traveling sound waves. A specific optical vortex can be strongly suppressed or amplified with a well-designed control light. The experimental results published in Science Advances show a significant vortex isolation rate, preventing random backscattering and signal degradation in the system.
“This is the first nonreciprocal system for vortex modes, which opens up a new perspective in nonreciprocal optics — the same physical effect can happen not only on the fundamental modes but also on higher-order modes” says Xinglin Zeng, the first author of this paper. “The light-driven optical vortex isolator will have great impact on the applications such as optical communications, quantum information processing, optical tweezers, and fiber lasers. I find the possibility of selective manipulation of vortex modes solely by light and sound waves a very fascinating concept” says Birgit Stiller, the leader of the Quantum Optoacoustics Research Group.
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Self-driving cars, like the human drivers that preceded them, need to see what’s around them to avoid obstacles and drive safely.
The most sophisticated autonomous vehicles typically use lidar, a spinning radar-type device that acts as the eyes of the car. Lidar provides constant information about the distance to objects so the car can decide what actions are safe to take.
But these eyes, it turns out, can be tricked.
New research reveals that expertly timed lasers shined at an approaching lidar system can create a blind spot in front of the vehicle large enough to completely hide moving pedestrians and other obstacles. The deleted data causes the cars to think the road is safe to continue moving along, endangering whatever may be in the attack’s blind spot.
This is the first time that lidar sensors have been tricked into deleting data about obstacles.
The vulnerability was uncovered by researchers from the University of Florida, the University of Michigan and the University of Electro-Communications in Japan. The scientists also provide upgrades that could eliminate this weakness to protect people from malicious attacks. More

Lithium-ion batteries are ubiquitous in the world of electric vehicles. However, a significant challenge encountered with their use is their low battery life and slow charging capability. Recent studies suggest two-dimensional (2D) nanomaterials to be a strong candidate for enhancing their performance. Recently, a collaborative research team from Japan and India demonstrated the efficacy of using 2D titanium diboride nanosheets in lithium-ion batteries. Their findings could have far-reaching consequences in the field of electric vehicles and other electronics.
As the electric vehicle (EV) industry is advancing, so are the efforts in the research and development of superior lithium (Li)-ion batteries to power these vehicles. Exploring and expanding rapid charge-discharge technology and extended battery life are critical challenges in their development. A few factors, such as the diffusion of Li ions, characteristics of the electrode-electrolyte interface, and electrode porosity, can help overcome these issues achieve extreme fast charging and ultralong life.
In recent years, two-dimensional (2D) nanomaterials, which are thin sheet-like structures with a thickness of a few nanometers, have emerged as potential anode materials for Li-ion batteries. These nanosheets possess a high aspect ratio and high density of active sites, which enables fast charging and superior cycling performance. In particular, 2D nanomaterials based on transition-metal diborides (or TMDs) have piqued the interest of researchers. TMDs have been found to have a high rate and long cycling stability for Li ion storage, owing to their honeycomb planes of boron and multivalent transition-metal atoms.
Recently, a group of scientists led by Prof. Noriyoshi Matsumi from the Japan Advanced Institute of Science and Technology (JAIST) and Prof. Kabeer Jasuja from the Indian Institite of Technology (IIT) Gandhinagar set out to further explore the potential of TMDs for energy storage. The team conducted the first experimental study on the storage potential of titanium diboride (TiB2)-based hierarchical nanosheets (THNS) as an anode material for Li-ion batteries. The team comprised Rajashekar Badam, former Senior Lecturer at JAIST; Akash Varma, former M.S. Course Student at JAIST; Koichi Higashimine, Technical Specialist at JAIST and Asha Liza James, Ph.D. Student at IIT Gandhinagar. Their study was published in ACS Applied Nano Materials and made available online on September 19, 2022.
The THNS were developed by oxidizing TiB2 powder with hydrogen peroxide, followed by centrifuging and freeze-drying the solution. “What makes our work stand out is the scalability of the method developed for synthesizing these TiB2 nanosheets. For any nanomaterial to translate into a tangible technology, scalability is the limiting factor. Our synthesis method only requires stirring and no sophisticated equipment. This is on account of the dissolution and recrystallization behavior exhibited by TiB2, a serendipitous discovery that makes this work a promising bridge from lab to the field,” explains Prof. Kabeer.
Thereafter, the team constructed an anodic Li-ion half-cell using the THNS as active anode material. The team studied the charge-storage characteristics of the THNS-based anodes.
The team found that the THNS-based anode showed a high discharge capacity of 380 mAh/g with a current density of just 0.025 A/g. Furthermore, they saw that a discharge capacity of 174 mAh/g could be obtained for a high current density of 1 A/g, with a charge time of 10 min and a capacity retention of 89.7% after 1,000 cycles. Additionally, the THNS-based Li-ion anode could sustain very high current rates, in the order of 15 to 20 A/g facilitating ultrafast charging in about 9 to 14 seconds. Under the high current rate, with a capacity retention greater than 80% was observed after 10,000 cycles.
The results of this study indicate the suitability of the 2D TiB2 nanosheets as a candidate for fast-charging and long-life Li-ion batteries. They also highlight the advantage of nano-scaling bulk materials, like TiB2, to attain promising properties, including pseudocapacitive charge storage, excellent high-rate capability, and superior cyclability. Explaining the potential long-term effects of their research, Prof. Matsumi says, “Such quick-charging technology can accelerate the diffusion of EVs and significantly decrease waiting times for charging various mobile electronic devices. We hope our findings can stimulate more research in this field, which can eventually lead to the convenience of EV users, lesser air pollution in cities, and less stressful mobile life in order to enhance the productivity of our society.”
Here’s hoping that we soon see this remarkable technology being used in EVs and other electronic devices. More

Artificial intelligence, “building-block” chemistry and a molecule-making machine teamed up to find the best general reaction conditions for synthesizing chemicals important to biomedical and materials research — a finding that could speed innovation and drug discovery as well as make complex chemistry automated and accessible.
With the machine-generated optimized conditions, researchers at the University of Illinois Urbana-Champaign and collaborators in Poland and Canada doubled the average yield of a special, hard-to-optimize type of reaction linking carbon atoms together in pharmaceutically important molecules. The researchers say their system provides a platform that also could be used to find general conditions for other classes of reactions and solutions for similarly complex problems. They reported their findings in the journal Science.
“Generality is critical for automation, and thus making molecular innovation accessible even to nonchemists,” said study co-leader Dr. Martin D. Burke, an Illinois professor of chemistry and of the Carle Illinois College of Medicine, as well as a medical doctor. “The challenge is the haystack of possible reaction conditions is astronomical, and the needle is hidden somewhere inside. By leveraging the power of artificial intelligence and building-block chemistry to create a feedback loop, we were able to shrink the haystack. And we found the needle.”
Automated synthesis machines for proteins and nucleic acids such as DNA have revolutionized research and chemical manufacturing in those fields, but many chemicals of importance for pharmaceutical, clinical, manufacturing and materials applications are small molecules with complex structures, the researchers say.
Burke’s group has pioneered the development of simple chemical building blocks for small molecules. His lab also developed an automated molecule-making machine that snaps together the buildings blocks to create a wide range of possible structures.
However, general reaction conditions to make the automated process broadly applicable have remained elusive. More

Topological materials, an exotic class of materials whose surfaces exhibit different electrical or functional properties than their interiors, have been a hot area of research since their experimental realization in 2007 — a finding that sparked further research and precipitated a Nobel Prize in Physics in 2016. These materials are thought to have great potential in a variety of fields, and might someday be used in ultraefficient electronic or optical devices, or key components of quantum computers.
But there are many thousands of compounds that may theoretically have topological characteristics, and synthesizing and testing even one such material to determine its topological properties can take months of experiments and analysis. Now a team of researchers at MIT and elsewhere have come up with a new approach that can rapidly screen candidate materials and determine with more than 90 percent accuracy whether they are topological.
Using this new method, the researchers have produced a list candidate materials. A few of these were already known to have topological properties, but the rest are newly predicted by this approach.
The findings are reported in the journal Advanced Materials in a paper by Mingda Li, the Class ’47 Career Development Professor at MIT, graduate students (and twin sisters) Nina Andrejevic at MIT and Jovana Andrejevic at Harvard University, and seven others at MIT, Harvard, Princeton University, and Argonne National Laboratory.
Topological materials are named after a branch of mathematics that describes shapes based on their invariant characteristics, which persist no matter how much an object is continuously stretched or squeezed out of its original shape. Topological materials, similarly, have properties that remain constant despite changes in their conditions, such as external perturbations or impurities.
There are several varieties of topological materials, including semiconductors, conductors, and semimetals, among others. Initially, it was thought that there were only a handful of such materials, but recent theory and calculations have predicted that in fact thousands of different compounds may have at least some topological characteristics. The hard part is figuring out experimentally which compounds may be topological. More

Researchers at the Max Planck Institute in Plön show that reputation plays a key role in determining which rewarding policies people adopt. Using game theory, they explain why individuals learn to use rewards to specifically promote good behaviour.
Often, we use positive incentives like rewards to promote cooperative behaviour. But why do we predominantly reward cooperation? Why is defection rarely rewarded? Or more generally, why do we bother to engage in any form of rewarding in the first place? Theoretical work done by researchers Saptarshi Pal and Christian Hilbe at the Max Planck Research Group ‘Dynamics of Social Behaviour’ suggests that reputation effects can explain why individuals learn to reward socially.
With tools from evolutionary game theory, the researchers construct a model where individuals in a population (the players) can adopt different strategies of cooperation and rewarding over time. In this model, the players’ reputation is a key element. The players know, with a degree of certainty (characterized by the information transmissibility of the population), how their interaction partners are going to react to their behaviour (that is, which behaviours they deem worthy of rewards). If the information transmissibility is sufficiently high, players learn to reward cooperation. In contrast, without sufficient information about peers, players refrain from using rewards. The researchers show that these effects of reputation also play out in a similar way when individuals interact in groups with more than two individuals.
Antisocial rewarding
In addition to highlighting the role of reputation in catalyzing cooperation and social rewarding, the scientists identify a couple of scenarios where antisocial rewarding may evolve. Antisocial rewarding either requires populations to be assorted or rewards to be mutually beneficial for both the recipient and the provider of the reward. “These conditions under which people may learn to reward defection are however a bit restrictive since they additionally require information to be scarce” adds Saptarshi Pal.
The results from this study suggest that rewards are only effective in promoting cooperation when they can sway individuals to act opportunistically. These opportunistic players only cooperate when they anticipate a reward for their cooperation. A higher information transmissibility increases both, the incentive to reward others for cooperating, and the incentive to cooperate in the first place. Overall, the model suggests that when people reward cooperation in an environment where information transmissibility is high, they ultimately benefit themselves. This interpretation takes the altruism out of social rewarding — people may not use rewards to enhance others’ welfare, but to help themselves.
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