Lifeboat Foundation

Kenya has stepped up efforts to find a local treatment for COVID-19. The Kenya Medical Research Institute (KEMRI), East Africa’s leading medical research facility, is testing the efficacy of an herbal medicine known as Zedupex.

The search for both a cure and a vaccine for the coronavirus has intensified around the globe, including in Kenya, as medical researchers race to find the elusive remedy.

Dr. Festus Tolo of Kenya’s Medical Research Institute is the lead scientist tasked with finding out whether an herbal-based drug will be effective against COVID-19. Zedupex, developed in 2015 by Kenyan researchers, has been used in the treatment of herpes.

Among the drugs used for COVID-19 is a particularly promising Cuban compound called “Interferon alfa-2b,” which has been successfully administered to Chinese patients.

In Cuba, where precaution measures against COVID-19 have included the temporary closing of schools and the shutdown of the tourist sector, among others, authorities have called on people to stay at home while Cuban scientists are working hard to develop new biotech products to kill the lethal disease.

With a growing number of cases, the island’s healthcare infrastructure is ready to treat an expected surge of patients with a wide selection of promising drugs. “The package of drugs we are administering is based on the experience of countries hardest hit by the disease. We have considered this with our experts, while our scientists try to add new local drugs to this protocol,” said Cuban Health Minister Jose Angel Portal in a recent TV appearance.

Machine learning performed by neural networks is a popular approach to developing artificial intelligence, as researchers aim to replicate brain functionalities for a variety of applications.

A paper in the journal Applied Physics Reviews, by AIP Publishing, proposes a new approach to perform computations required by a neural network, using light instead of electricity. In this approach, a photonic tensor core performs multiplications of matrices in parallel, improving speed and efficiency of current deep learning paradigms.

In machine learning, neural networks are trained to learn to perform unsupervised decision and classification on unseen data. Once a neural network is trained on data, it can produce an inference to recognize and classify objects and patterns and find a signature within the data.

Tailoring and manipulating electromagnetic wave propagation has been of great interest within the scientific community for many decades. In this context, wave propagation has been engineered by properly introducing spatial inhomogeneities along the path where the wave is traveling. Antennas and communications systems in general have greatly benefited from this wave-matter control. For instance, if one needs to re-direct the radiated field (information) from an antenna (transmitter) to a desired direction and reach a receiving antenna placed at a different location, one can simply place the former in a translation stage and mechanically steer the propagation of the emitted electromagnetic wave.

Such beam steering techniques have greatly contributed to the spatial aiming of targets in applications such as radars and point-to-point communication systems. Beam steering can also be achieved using metamaterials and metasurfaces by means of spatially controlling the effective electromagnetic parameters of a designed meta-lens antenna system and/or using reconfigurable meta-surfaces. The next question to ask: Could we push the limits of current beam steering applications by controlling electromagnetic properties of media not only in space but also in time (i.e., 4D metamaterials x, y,z, t)? In order words, would it be possible to achieve temporal aiming of electromagnetic waves?

In a new paper published in Light Science & Applications, Victor Pacheco-Peña from the School of Mathematics, Statistics and Physics of Newcastle University in UK and Nader Engheta from and Department of Electrical and Systems Engineering of the University of Pennsylvania, USA have answered this question by proposing the idea of temporal metamaterials that change from an isotropic to an anisotropic permittivity tensor. In this concept, the authors consider a rapid change of the permittivity of the whole medium where the wave is traveling and demonstrated both numerically and analytically the effects of such a temporal boundary caused by the rapid temporal change of permittivity. In so doing, forward and backward waves are produced with wave vector k preserved through the whole process while frequency is changed, depending on the values of the permittivity tensor before and after the temporal change of permittivity.

However, to dismiss the subject as fantastical or unnecessary would be akin to telling scientists 100 years ago that landing on the moon was also irrelevant.

This is because, for pioneers and champions of artificial intelligence, quantum computing is the holy grail. It’s not a make-believe fantasy; rather, it’s a tangible area of science that will take our probability-driven world into a whole new dimension.

Spiral galaxies such as our Milky Way can have sprawling magnetic fields. There are various theories about their formation, but so far the process is not well understood. An international research team has now analyzed the magnetic field of the Milky Way-like galaxy NGC 4217 in detail on the basis of radio astronomical observations and has discovered as yet unknown magnetic field structures. The data suggest that star formation and star explosions, so-called supernovae, are responsible for the visible structures.

The team led by Dr. Yelena Stein from Ruhr-Universität Bochum, the Centre de Données astronomiques de Strasbourg and the Max Planck Institute for Radio Astronomy in Bonn together with US-American and Canadian colleagues, published their report in the journal Astronomy and Astrophysics, released online on 21 July 2020.

The analyzed data had been compiled in the project “Continuum Halos in Nearby Galaxies”, where radio waves were utilized to measure 35 galaxies. “Galaxy NGC 4217 is of particular interest to us,” explains Yelena Stein, who began the study at the Chair of Astronomy at Ruhr-Universität Bochum under Professor Ralf-Jürgen Dettmar and who currently works at the Centre de Données astronomiques de Strasbourg. NGC 4217 is similar to the Milky Way and is only about 67 million light years away, which means relatively close to it, in the Ursa Major constellation. The researchers therefore hope to successfully transfer some of their findings to our home galaxy.

A blood test has been shown to detect five types of cancer years before the diseases could be spotted using conventional diagnostic methods, according to a study published Tuesday.

Developed by a Sino-US startup, the test found cancers in 91 percent of people who showed no symptoms when the blood sample was collected but were diagnosed one-to-four years later with stomach, esophageal, colon, lung or liver cancer, researchers reported in Nature Communications.

“The immediate focus is to test people at higher risk, based on family history, age or other known risk factors,” said co-author Kun Zhang, head of the bioengineering department at the University of California San Diego and an equity holder in Singlera Genomics, which developed the test.

The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorized internet writings, and basically guesses what text ought to come next from any starting point. That may sound unglamorous, but a language model built for guessing with 175 billion parameters — 10 times more than previous competitors — is surprisingly powerful.

With attention focused on a pandemic and an election, AI has taken a major leap forward.

David Malan, of the hit class CS50, was working to perfect online teaching long before the pandemic. Is his method a model for the future of higher education?