Machine learning create hyper predictive computer models

Drug development is a costly and time-consuming process. To narrow down the number of chemical compounds that could be potential drug candidates, scientists utilize computer models that can predict how a particular chemical compound might interact with a biological target of interest — for example, a key protein that might be involved with a disease process. Traditionally, this is done via quantitative structure-activity relationship (QSAR) modeling and molecular docking, which rely on 2- and 3-D information about those chemicals.

Denis Fourches, assistant professor of computational chemistry, wanted to improve upon the accuracy of these QSAR models. “When you’re screening a set of 30 million compounds, you don’t necessarily need a very high reliability with your model — you’re just getting a ballpark idea about the top 5 or 10 percent of that virtual library. But if you’re attempting to narrow a field of 200 analogues down to 10, which is more commonly the case in drug development, your modeling technique must be extremely accurate. Current techniques are definitely not reliable enough.”

Fourches and Jeremy Ash, a graduate student in

The research of quantum technologies

A central concept in quantum mechanics is that of energy level. When a quantum mechanical system, such as an atom, absorbs a quantum of energy from light, it becomes excited from a lower to a higher energy level. Changing the separation between the energy levels is called frequency modulation. In quantum devices, frequency modulation is utilized in controlling interactions, inducing transitions among quantum states and engineering artificial energy structures.

“The basis of quantum mechanical frequency modulation is known since the 1930s. However, the breakthrough of various quantum technologies in 2000s has created a need for understanding and better theoretical tools of quantum systems under frequency modulation,” says Matti Silveri, presently a postdoctoral researcher from University of Oulu.

Understanding and utilization of frequency modulation is important for developing more accurate quantum devices and faster quantum gates for the near-future small scale quantum computers. The research field of quantum devices and computing is rapidly growing and it has recently attracted also investments from major technology companies, such as, from Google, Intel, IBM and Microsoft.

“We wanted to review the recent

A new route to molecular wires

As conventional silicon-integrated circuits reach their lower size limit, new concepts are required such as molecular electronics — the use of electronic components comprised of molecular building blocks. Shuo-Wang Yang at A*STAR Institute of High Performance Computing together with his colleagues and collaborators, are using computer modeling to design electric wires made of polymer chains.

“It has been a long-standing goal to make conductive molecular wires on traditional semiconductor or insulator substrates to satisfy the ongoing demand miniaturization in electronic devices,” explains Yang.

Progress has been delayed in identifying molecules that both conduct electricity and bind to substrates. “Structures with functional groups that facilitate strong surface adsorption typically exhibit poor electrical conductivity, because charge carriers tend to localize at these groups,” he adds.

Yang’s team applied density functional theory to a two-step approach for synthesizing linear polymer chains on a silicon surface. “This theory is the best simulation method for uncovering the mechanism behind chemical reactions at atomic and electronic levels. It can be used to predict the reaction pathways to guide researchers,” says Yang.

The first

Heart disease designed by new digital instrument

“Exercise reduces cardiovascular risk, improves body composition and physical fitness, and lowers mortality and morbidity,” said lead author Professor Dominique Hansen, associate professor in exercise physiology and rehabilitation of internal diseases at Hasselt University, Diepenbeek, Belgium. “But surveys have shown that many clinicians experience great difficulties in prescribing specific exercise programmes for patients with multiple cardiovascular diseases and risk factors.”

The European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool generates exercise prescriptions for patients with different combinations of cardiovascular risk factors or cardiovascular diseases. The tool was designed by cardiovascular rehabilitation specialists from 11 European countries, in close collaboration with computer scientists from Hasselt University.

EXPERT can be installed on a laptop or personal computer (PC). During a consultation, the clinician inputs the patient’s characteristics and cardiovascular risk factors, cardiovascular diseases and other chronic conditions, medications, adverse events during exercise testing, and physical fitness (from a cardiopulmonary exercise test).

The tool automatically designs a personalized exercise programme for the patient. It includes the ideal exercise type, intensity, frequency, and duration of each session. Safety precautions are also given for patients with certain conditions. The advice can be printed out and given to the patient to

A study comparing acceptance rates

“There are a number of questions and concerns related to gender bias in computer programming, but this project was focused on one specific research question: To what extent does gender bias exist when pull requests are judged on GitHub?” says Emerson Murphy-Hill, corresponding author of a paper on the study and an associate professor of computer science at North Carolina State University.

GitHub is an online programming community that fosters collaboration on open-source software projects. When people identify ways to improve code on a given project, they submit a “pull request.” Those pull requests are then approved or denied by “insiders,” the programmers who are responsible for overseeing the project.

For this study, researchers looked at more than 3 million pull requests from approximately 330,000 GitHub users, of whom about 21,000 were women.

The researchers found that 78.7 percent of women’s pull requests were accepted, compared to 74.6 percent for men.

However, when looking at pull requests by people who were not insiders on the relevant project, the results got more complicated.

Programmers who could easily be identified as women based on their names or profile pictures had lower pull request acceptance rates (58 percent) than users who could be identified as men (61 percent).

Artificial intelligence shows potential to fight

In a study published online in Ophthalmology, the journal of the American Academy of Ophthalmology, the researchers describe how they used deep-learning methods to create an automated algorithm to detect diabetic retinopathy. Diabetic retinopathy (DR) is a condition that damages the blood vessels at the back of the eye, potentially causing blindness.

“What we showed is that an artificial intelligence-based grading algorithm can be used to identify, with high reliability, which patients should be referred to an ophthalmologist for further evaluation and treatment,” said Theodore Leng, M.D., lead author. “If properly implemented on a worldwide basis, this algorithm has the potential to reduce the workload on doctors and increase the efficiency of limited healthcare resources. We hope that this technology will have the greatest impact in parts of the world where ophthalmologists are in short supply.”

Another advantage is that the algorithm does not require any specialized, inaccessible, or costly computer equipment to grade images. It can be run on a common personal computer or smartphone with average processors.

Deep learning is on the rise in computer science and medicine because it can teach computers to do what our brains do naturally. What Dr. Leng and his colleagues did was to create an

Positioning quantum bits in diamond optical circuits

But practical, diamond-based quantum computing devices will require the ability to position those defects at precise locations in complex diamond structures, where the defects can function as qubits, the basic units of information in quantum computing. In Nature Communications, a team of researchers from MIT, Harvard University, and Sandia National Laboratories reports a new technique for creating targeted defects, which is simpler and more precise than its predecessors.

In experiments, the defects produced by the technique were, on average, within 50 nanometers of their ideal locations.

“The dream scenario in quantum information processing is to make an optical circuit to shuttle photonic qubits and then position a quantum memory wherever you need it,” says Dirk Englund, an associate professor of electrical engineering and computer science who led the MIT team. “We’re almost there with this. These emitters are almost perfect.”

The new paper has 15 co-authors. Seven are from MIT, including Englund and first author Tim Schröder, who was a postdoc in Englund’s lab when the work was done and is now an assistant professor at the University of Copenhagen’s Niels Bohr Institute. Edward Bielejec led the Sandia team, and physics professor Mikhail Lukin led the Harvard team.

Appealing defects

Quantum computers, which are still

Practicing brain surgery

A report on the simulator that guides trainees through an endoscopic third ventriculostomy (ETV) was published in the Journal of Neurosurgery: Pediatrics on April 25. The procedure uses endoscopes, which are small, computer-guided tubes and instruments, to treat certain forms of hydrocephalus, a condition marked by an excessive accumulation of cerebrospinal fluid and pressure on the brain. ETV is a minimally invasive procedure that short-circuits the fluid back into normal channels in the brain, eliminating the need for implantation of a shunt, a lifelong device with the associated complications of a foreign body.

“For surgeons, the ability to practice a procedure is essential for accurate and safe performance of the procedure. Surgical simulation is akin to a golfer taking a practice swing,” says Alan R. Cohen, M.D., professor of neurosurgery at the Johns Hopkins University School of Medicine and a senior author of the report. “With surgical simulation, we can practice the operation before performing it live.”

While cadavers are the traditional choice for such surgical training, Cohen says they are scarce, expensive, nonreusable, and most importantly, unable to precisely simulate the experience of operating on the problem at hand, which Cohen says requires a special type of hand-eye coordination he dubs

New capacity for electronics

That technology is still science fiction, but a new study may bring it closer to reality. A team of researchers from Japan reports in Applied Physics Letters, from AIP Publishing, that they have discovered a phenomenon called the photodielectric effect, which could lead to laser-controlled touch displays.

A number of basic circuit components have been developed beyond their traditional electricity-based designs to instead be controlled with light, such as photo-resistors, photodiodes, and phototransistors. However, there isn’t yet a photo-capacitor.

“A photo-capacitor provides a novel way for operating electronic devices with light,” said Hiroki Taniguchi of the University of Nagoya in Japan. “It will push the evolution of electronics to next-generation photo-electronics.”

Capacitors are basic components for all kinds of electronics, acting somewhat like buckets for electrons that can, for example, store energy or filter unwanted frequencies. Most simply, a capacitor consists of two parallel conducting plates separated by an electrically insulating material, called a dielectric, such as air or glass. Applying a voltage across the plates causes opposing (and equal) charges to build up on both plates.

The dielectric’s properties play a determinate role in the electric field profile between the plates and, in turn, how much energy the capacitor can store. By using

Data analysis support public health decision making

“In a real-world outbreak, the time is often too short and the data too limited to build a really accurate model to map disease progression or guide public-health decisions,” said Ashlynn R. Daughton, a graduate research assistant at Los Alamos and doctoral student at University of Colorado, Boulder. She is lead author on a paper out last week in Scientific Reports, a Nature journal. “Our aim is to use existing models with low computational requirements first to explore disease-control measures and second to develop a platform for public-health collaborators to use and provide feedback on models,” she said.

The research draws on Los Alamos’ expertise in computational modeling and health sciences and contributes to the Laboratory’s national security mission by protecting against biological threats. Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a highly subjective process that involves both surveillance and expert opinion. Epidemiological modeling can fill gaps in the decision-making process, she says, by using available data to provide quantitative estimates of outbreak trajectories — determining where the infection is going, and how fast, so medical supplies and staff can be deployed for maximum effect. But if

Help extend Moores Law

In the world of semiconductor physics, the goal is to devise more efficient and microscopic ways to control and keep track of 0 and 1, the binary codes that all information storage and logic functions in computers are based on.

A new field of physics seeking such advancements is called valleytronics, which exploits the electron’s “valley degree of freedom” for data storage and logic applications. Simply put, valleys are maxima and minima of electron energies in a crystalline solid. A method to control electrons in different valleys could yield new, super-efficient computer chips.

A University at Buffalo team, led by Hao Zeng, PhD, professor in the Department of Physics, worked with scientists around the world to discover a new way to split the energy levels between the valleys in a two-dimensional semiconductor.

The work is described in a study published online today (May 1, 2017) in the journal Nature Nanotechnology.

The key to Zeng’s discovery is the use of a ferromagnetic compound to pull the valleys apart and keep them at different energy levels. This leads to an increase in the separation of valley energies by a factor of 10 more than the one obtained by applying an external magnetic field.

“Normally

Memristor chips that see patterns over pixels

Faster image processing could have big implications for autonomous systems such as self-driving cars, says Wei Lu, U-M professor of electrical engineering and computer science. Lu is lead author of a paper on the work published in the current issue of Nature Nanotechnology.

Lu’s next-generation computer components use pattern recognition to shortcut the energy-intensive process conventional systems use to dissect images. In this new work, he and his colleagues demonstrate an algorithm that relies on a technique called “sparse coding” to coax their 32-by-32 array of memristors to efficiently analyze and recreate several photos.

Memristors are electrical resistors with memory — advanced electronic devices that regulate current based on the history of the voltages applied to them. They can store and process data simultaneously, which makes them a lot more efficient than traditional systems. In a conventional computer, logic and memory functions are located at different parts of the circuit.

“The tasks we ask of today’s computers have grown in complexity,” Lu said. “In this ‘big data’ era, computers require costly, constant and slow communications between their processor and memory to retrieve large amounts data. This makes them large, expensive and power-hungry.”

But like neural networks in a biological brain, networks of memristors can