Lifeboat Foundation

Skoltech researchers have offered a solution to the problem of searching for materials with required properties among all possible combinations of chemical elements. These combinations are virtually endless, and each has an infinite multitude of possible crystal structures; it is not feasible to test them all and choose the best option (for instance, the hardest compound) either in an experiment or in silico. The computational method developed by Skoltech professor Artem R. Oganov and his PhD student Zahed Allahyari solves this major problem of theoretical materials science. Oganov and Allahyari presented their method in the MendS code (stands for Mendelevian Search) and tested it on superhard and magnetic materials.

“In 2006, we developed an algorithm that can predict the crystal structure of a given fixed combination of chemical elements. Then we increased its predictive powers by teaching it to work without a specific combination — so one calculation would give you all stable compounds of given elements and their respective crystal structures. The new method tackles a much more ambitious task: here, we pick neither a precise compound nor even specific chemical elements — rather, we search through all possible combinations of all chemical elements, taking into account all possible crystal structures, and find those that have the needed properties (e.g., highest hardness or highest magnetization)” says Artem Oganov, Skoltech and MIPT professor, Fellow of the Royal Society of Chemistry and a member of Academia Europaea.

The researchers first figured out that it was possible to build an abstract chemical space so that compounds that would be close to each other in this space would have similar properties. Thus, all materials with peculiar properties (for example, superhard materials) will be clustered in certain areas, and evolutionary algorithms will be particularly effective for finding the best material. The Mendelevian Search algorithm runs through a double evolutionary search: for each point in the chemical space, it looks for the best crystal structure, and at the same time these found compounds compete against each other, mate and mutate in a natural selection of the best one.

The global pandemic of COVID-19 cases caused by infection with SARS-CoV-2 is ongoing, with no approved antiviral intervention. We describe here the effects of treatment with interferon (IFN)-α2b in a cohort of confirmed COVID-19 cases in Wuhan, China. In this uncontrolled, exploratory study, 77 adults hospitalized with confirmed COVID-19 were treated with either nebulized IFN-α2b (5 mU b.i.d.), arbidol (200 mg t.i.d.) or a combination of IFN-α2b plus arbidol. Serial SARS-CoV-2 testing along with hematological measurements, including cell counts, blood biochemistry and serum cytokine levels, and temperature and blood oxygen saturation levels, were recorded for each patient during their hospital stay. Treatment with IFN-α2b with or without arbidol significantly reduced the duration of detectable virus in the upper respiratory tract and in parallel reduced duration of elevated blood levels for the inflammatory markers IL-6 and CRP. These findings suggest that IFN-α2b should be further investigated as a therapy in COVID-19 cases.

In December 2019, an outbreak of pneumonia was reported in Wuhan, Hubei province, China, resulting from infection with a novel coronavirus (CoV), severe acute respiratory syndrome (SARS)-CoV-2. SARS-CoV-2 is a novel, enveloped betacoronavirus with phylogenetic similarity to SARS-CoV (1). Unlike the coronaviruses HCoV-229E, HCoV-OC43, HCoV-NL63, and HCoV-HKU, that are pathogenic in humans and are associated with mild clinical symptoms, SARS-CoV-2 resembles both SARS-CoV and Middle East respiratory syndrome (MERS), with the potential to cause more severe disease. A critical distinction is that CoVs that infect the upper respiratory tract tend to cause a mild disease, whereas CoVs that infect both upper and lower respiratory tracts (such as SARS-CoV-2 appears to be) may cause more severe disease. Coronavirus disease (COVID)-19, the disease caused by SARS-CoV-2, has since spread around the globe as a pandemic.

In the absence of a SARS-CoV-2-specific vaccine or an approved antiviral, a number of antivirals are currently being evaluated for their therapeutic effectiveness. Type I IFNs-α/β are broad spectrum antivirals, exhibiting both direct inhibitory effects on viral replication and supporting an immune response to clear virus infection (2). During the 2003 SARS-CoV outbreak in Toronto, Canada, treatment of hospitalized SARS patients with an IFN-α, resulted in accelerated resolution of lung abnormalities (3). Arbidol (ARB) (Umifenovir) (ethyl-6-bromo-4-[(dimethylamino)methyl]-5-hydroxy-1-methyl-2 [(phenylthio)methyl]-indole-3-carboxylate hydrochloride monohydrate), a broad spectrum direct-acting antiviral, induces IFN production and phagocyte activation. ARB displays antiviral activity against respiratory viruses, including coronaviruses (4).

High-fidelity modelling of nanosecond repetitively pulsed discharges (NRPDs) is burdened by the multiple time and length scales and large chemistry mechanisms involved, which prohibit detailed analyses and parametric studies. In the present work, we propose a ‘frozen electric-field’ modelling approach to expedite the NRPD simulations without adverse effects on the solution accuracy. First, a burst of nanosecond voltage pulses is simulated self-consistently until the discharge reaches a stationary state. The calculated spatial distributions and temporal evolution of the electric field, electron density and electron energy during the last pulse are then stored in a library and the electrical characteristics of subsequent pulses are frozen at these values. This strategy allows the timestep for numerical integration to be increased by four orders of magnitude (from 10⁻¹³ to 10⁻⁹ s), thereby significantly improving the computational efficiency of the process. Reduced calculations of a burst of 50 discharge pulses show good agreement with the predictions from a complete plasma model (electrical characteristics calculated during each pulse). The error in species densities is less than 20% at the centre of the discharge volume and about 30% near the boundaries. The deviations in temperature, however, are much lower, at 5% in the entire domain. The model predictions are in excellent agreement with measured ignition delay times and temperatures in H₂–air mixtures subject to dielectric barrier NRPD over a pressure range of 54–144 Torr with equivalence ratios of 0.7–1.2. The OH density increases with pressure and triggers low-temperature fuel oxidation, which leads to rapid temperature rise and ignition. The ignition delay decreases by a factor of 2, with an increase in pressure from 54 to 144 Torr. In contrast, an increase in the H₂–air equivalence ratio from 0.7 to 1.2 marginally decreases the ignition delay by about 20%. This behaviour is attributed to the insensitivity of OH production rates to the variation in the equivalence ratio.

Carbon nanotubes embedded in leaves detect chemical signals that are produced when a plant is damaged.

MIT engineers have developed a way to closely track how plants respond to stresses such as injury, infection, and light damage, using sensors made of carbon nanotubes. These sensors can be embedded in plant leaves, where they report on hydrogen peroxide signaling waves.

Plants use hydrogen peroxide to communicate within their leaves, sending out a distress signal that stimulates leaf cells to produce compounds that will help them repair damage or fend off predators such as insects. The new sensors can use these hydrogen peroxide signals to distinguish between different types of stress, as well as between different species of plants.

Over billions of years, microorganisms and plants evolved the remarkable process we know as photosynthesis. Photosynthesis converts sun energy into chemical energy, thus providing all life on Earth with food and oxygen. The cellular compartments housing the molecular machines, the chloroplasts, are probably the most important natural engines on earth. Many scientists consider artificially rebuilding and controlling the photosynthetic process the “Apollo project of our time.” It would mean the ability to produce clean energy—clean fuel, clean carbon compounds such as antibiotics, and other products simply from light and carbon dioxide.

But how to build a living, photosynthetic cell from scratch? Key to mimicking the processes of a living cell is to get its components to work together at the right time and place. At the Max Planck Society, this ambitious goal is pursued in an interdisciplinary multi-lab initiative, the MaxSynBio network. Now the Marburg research team led by director Tobias Erb has succeeded successfully created a platform for the automated construction of cell-sized photosynthetically active compartments, “artificial chloroplasts,” that are able to capture and convert the greenhouse gas carbon dioxide with light.

According to Li and Spitzer, running on a treadmill, or performing another sustained aerobic exercise—like dancing or kickboxing—on a regular basis might actually enhance motor skill-based learning.

When comparing the brains of mice that exercised versus those who did not, Li and Spitzer found that specific neurons switched their chemical signals (neurotransmitters), after exercising, which led to improved learning for motor skill-specific acquisition.

While physical exercise is proven to promote motor skill learning in normal individuals as well as those with neurological disorders, the mechanism of action is unclear. The study found that that one just week of voluntary wheel running enhances the acquisition of motor skills in normal adult mice. Voluntary being the keyword here.

Writing in the journal NanoResearch, a team at the University of Massachusetts Amherst reports this week that they have developed bioelectronic ammonia gas sensors that are among the most sensitive ever made.

The sensor uses electric-charge-conducting protein nanowires derived from the bacterium Geobacter to provide biomaterials for electrical devices. More than 30 years ago, senior author and microbiologist Derek Lovley discovered Geobacter in river mud. The microbes grow hair-like protein filaments that work as nanoscale “wires” to transfer charges for their nourishment and to communicate with other bacteria.

First author and biomedical engineering doctoral student Alexander Smith, with his advisor Jun Yao and Lovley, say they designed this first sensor to measure ammonia because that gas is important to agriculture, the environment and biomedicine. For example, in humans, ammonia on the breath may signal disease, while in poultry farming, the gas must be closely monitored and controlled for bird health and comfort and to avoid feed imbalances and production losses.

In the movie “Transformers,” cars morph into robots, jets or a variety of machinery. A similar concept inspired a group of researchers to combine gas foaming, which is a blend of chemicals that induces gas bubbling, and 3D molding technologies to quickly transform electrospun membranes into complex 3D shapes for biomedical applications.

In Applied Physics Reviews, the group reports on its new approach that demonstrates significant improvements in speed and quality compared with other methods. The work is also the first successful demonstration of formation of 3D neural tissue constructs with an ordered structure through differentiation of human neural progenitor/stem cells on these transformed 3D nanofiber scaffolds.

“Electrospinning is a technology to produce nanofiber membranes,” said co-author Jingwei Xie, at the University of Nebraska Medical Center. “The physics principle behind it involves applying an electrical force to overcome the surface tension of a solution to elongate a solution jet into continuous and ultrafine fibers after solvent evaporation.”

If you are interested in age reversal, and you haven’t read Dr David Sinclair (Harvard Medical School) yet, then I’d recommend this research paper.

“Excitingly, new studies show that age-related epigenetic changes can be reversed with interventions such as cyclic expression of the Yamanaka reprogramming factors. This review presents a summary of epigenetic changes that occur in aging, highlights studies indicating that epigenetic changes may contribute to the aging process and outlines the current state of research into interventions to reprogram age-related epigenetic changes.”

The aging process results in significant epigenetic changes at all levels of chromatin and DNA organization. These include reduced global heterochromatin, nucleosome remodeling and loss, changes in histone marks, global DNA hypomethylation with CpG island hypermethylation, and the relocalization of chromatin modifying factors. Exactly how and why these changes occur is not fully understood, but evidence that these epigenetic changes affect longevity and may cause aging, is growing. Excitingly, new studies show that age-related epigenetic changes can be reversed with interventions such as cyclic expression of the Yamanaka reprogramming factors. This review presents a summary of epigenetic changes that occur in aging, highlights studies indicating that epigenetic changes may contribute to the aging process and outlines the current state of research into interventions to reprogram age-related epigenetic changes.

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