Tyler O'Neal, Staff Editor GOVERNMENT

Russian team finds alternatives to diamonds for drilling

Using computational methods, scientists have plotted a highly accurate map to guide the synthesis of new, cheaper materials tough enough for the mining and space industries

Diamonds aren't just a girl's best friend -- they're also crucial components for hard-wearing industrial components, such as the drill bits used to access oil and gas deposits underground. But a cost-efficient method to find other suitable materials to do the job is on the way.

Diamond is one of the only materials hard and tough enough for the job of constant grinding without significant wear, but as any imminent proposee knows, diamonds are pricey. High costs drive the search for new hard and superhard materials. However, the experimental trial-and-error search is itself expensive.  {module In-article}

A simple and reliable way to predict new material properties is needed to facilitate modern technology development. Using a computational algorithm, Russian theorists have published just such a predictive tool in the Journal of Applied Physics, from AIP Publishing.

"Our study outlines a picture that can guide experimentalists, showing them the direction to search for new hard materials," said the study's first author Alexander Kvashnin, from the Skolkovo Institute of Science and Technology and Moscow Institute of Physics and Technology.

As fiber optics, with its fast transmission rate, replaced copper wire communications, so too do materials scientists search to find new materials with desirable properties to support modern technology. When it comes to the mining, space and defense industries, it's all about finding materials that don't break easily, and for that, the optimal combination of hardness and fracture toughness is required. But it's tricky to theoretically predict hardness and fracture toughness. Kvashnin explained that although lots of predictive models exist, he estimates they are 10%-15% out off the mark at best.

The Russian team recently developed a computational approach that considers all possible combinations of elements in Dmitri Mendeleev's periodic table -- christened "Mendelevian search." They've used their algorithm to search for optimal hard and tough materials.

By combining their toughness prediction model with two well-known models for material hardness, the scientists' algorithm learned which regions of chemical space of compounds were most promising for tough, hard phases that could be easily synthesized.

Results were plotted on a "treasure map" of toughness vs. hardness, and the scientists were impressed by what they saw. All known hard materials were predicted with more than 90% accuracy. This proved the search's predictive power, and the newly revealed combinations are potential treasures for industry.

Kvashnin explained he is part of an industrial project devoted to new materials for drilling bits, where experimentalists are now synthesizing one of these hard material treasures -- tungsten pentaboride (WB5).

"This computational search is a potential way to optimize the search for new materials, much cheaper, faster and quite accurately," said Kvashnin, who hopes that this new approach will enable the speedy development of new materials with enhanced properties.

But they aren't stopping there with the theory. They want to use their modern methods and approaches to pin down the general rules for what makes hard and superhard materials among the elements to better guide researchers of the future.

Israeli researchers develop algorithm to predict infectious diseases

Tyler O'Neal, Staff Editor GOVERNMENT

An algorithm that predicts the immune response to a pathogen could lead to early diagnosis for such diseases as tuberculosis

First impressions are important - they can set the stage for the entire course of a relationship. The same is true for the impressions the cells of our immune system form when they first meet a new bacterium. Using this insight, Weizmann Institute of Science researchers have developed an algorithm that may predict the onset of such diseases as tuberculosis. The findings of this research were recently published in Nature Communications.

Dr. Roi Avraham, whose group in the Institute's Biological Regulation Department conducted the research, explains: "When immune cell and bacterium meet, there can be several outcomes. The immune system can kill the bacteria; the bacteria can overcome the immune defenses; or, in the case of diseases like tuberculosis, the bacterium can lie dormant for years, sometimes causing disease at a later stage and sometimes remaining in hibernation for good. We think that the junction in which one of those paths is chosen takes place early on - some 24-48 hours after infection." The repertoire of immune cells taken from blood samples after exposure to a bacterium (inside the circle, cell types; outside, subtypes) and their activation levels. Based on an algorithm, the information can be obtained from a normal blood test with no need for expensive genetic single-cell sequencing{module In-article}

The scientists first tested real meetings between immune cells and bacteria - this time between blood samples (which contain immune cells) and the Salmonella bacterium. Led by Drs. Noa Bossel Ben Moshe and Shelly Hen-Avivi in Avraham's group, the research team used a method that has been developed in recent years, at the Weizmann Institute among other places, to sequence the gene activity in thousands of individual cells. In other words, they could see what each cell looked like as it responded to the Salmonella bacteria and they could map out the activation profiles of each. This, indeed revealed patterns not seen in standard lab tests, and it seemed to confirm their hypothesis - there were indeed, differences that enabled them to trace responses from the initial meetings to the later outcomes.

Such single-cell sequencing is still limited to specialized labs, however. The group asked whether there was a way to connect their results to real-time blood tests in real patients. For this, they turned to their single cell databases on Salmonella infection and immune responses and developed an algorithm - based on a method known as deconvolution - that would then enable them to extract similar information from standard data sets. This algorithm uses information available from the standard blood tests and extrapolates to the properties of the individual blood cells in the experiments. "The algorithm we developed," says Bossel Ben Moshe, "can not only define the ensemble of immune cells that take part in the response, it can reveal their activity levels and thus the potential strength of the immune response."

The first test of the algorithm was in blood samples taken from healthy people in the Netherlands. These samples were infected, in a lab dish, with Salmonella bacteria, and the immune response recorded. Comparisons with existing genomic analysis methods showed that the standard methods did not uncover differences between groups, while the algorithm the group had developed revealed significant ones that were tied to later variations in bacteria-killing abilities.

The group then asked whether the same algorithm could be used to diagnose the onset of tuberculosis, which is caused by a bacterium that often chooses the third way - dormancy -- and thus can hide out in the body for years. Up to a third of the world's population carries the tuberculosis bacterium, though only a small percentage of these actually become ill. Still, some two million die of the disease each year, mostly in underdeveloped areas of China, Russia and Africa. The researchers turned to another database - a British one that followed patients and carriers for a period of two years -- so the group could apply the algorithm to blood test results from both groups, as well as from the subset who went from carrier to disease onset during that period.

The researchers found that the activity levels of immune cells called monocytes could be used to predict the onset or course of the disease. "The algorithm is based on the 'first impressions' of immune cells and Salmonella, which cause a very different type of illness than mycobacterium tuberculosis," says Hen-Avivi. "Still, we were able to predict, early on, which of the carriers would develop the active form of the disease."

Once tuberculosis symptoms appear, patients have to take three different antibiotics over the course of nine months, and antibiotic resistance has become rampant in these bacteria. "If those who are at risk of active disease could be identified when the bacterial load is smaller, their chances of recovery will be better," says Avraham. "And the state medical systems in countries where tuberculosis is endemic might have a better way to keep the suffering and incidence of sickness down while reducing the cost of treatment."

The researchers intend to continue in this line of research - to expand their own database on tuberculosis and other pathogens so to as to refine the algorithm and work on developing the tools that may, in the future, be used to predict who will develop full-blown disease. With the refined algorithm further avenues of research may lead to methods of predicting the course of a number of infectious diseases.

Chest X-rays contain information that can be harvested with AI

Tyler O'Neal, Staff Editor GOVERNMENT

Study finds chest X-rays contain 'hidden' information that can be harvested with artificial intelligence to predict long-term mortality

The most frequently performed imaging exam in medicine "the chest X-ray" holds 'hidden' prognostic information that can be harvested with artificial intelligence (AI), according to a study by scientists at Massachusetts General Hospital (MGH) in Boston. The findings of this study published in the July 19, 2019 issue of JAMA Network Open, could help to identify patients most likely to benefit from screening and preventive medicine for heart disease, lung cancer and other conditions.  {module In-article}

AI is responsible for major advances in medicine; for example, several groups have applied AI to automate diagnosis of chest X-rays for detection of pneumonia and tuberculosis. 

If this technology can make diagnoses, asked radiologist Michael Lu, MD, MPH, could it also identify people at high risk for future heart attack, lung cancer, or death? Lu, who is director of research for the MGH Division of Cardiovascular Imaging and assistant professor of Radiology at Harvard Medical School, and his colleagues developed a convolutional neural network, a state-of-the-art AI tool for analyzing visual information, called CXR-risk. CXR-risk was trained by having the network analyze more than 85,000 chest X-rays from 42,000 subjects who took part in an earlier clinical trial. Each image was paired with a key piece of data: Did the person die over a 12-year period? The goal was for CXR-risk to learn the features or combinations of features on a chest X-ray image that best predict health and mortality. 

Next, Lu and colleagues tested CXR-risk using chest X-rays for 16,000 patients from two earlier clinical trials. They found that 53% of people the neural network identified as "very high risk" died over 12 years, compared to fewer than 4% of those that CXR-risk labeled as "very low risk." The study found that CXR-risk provided information that predicts long-term mortality, independent of radiologists' readings of the x-rays and other factors, such as age and smoking status.

Lu believes this new tool will be even more accurate when combined with other risk factors, such as genetics and smoking status. Early identification of at-risk patients could get more into preventive and treatment programs. "This is a new way to extract prognostic information from everyday diagnostic tests," says Lu. "It's information that's already there that we're not using, that could improve people's health."