Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 4320

When Lady Meherbai Tata died of leukaemia on 18 June 1931, her husband, Sir Dorabji Tata, Jamsetji Tata’s son and a key figure of the Tata Group endowed the Lady Tata Memorial Trust with a corpus for research into leukaemia in memory of his wife. He set out to establish high-quality facilities for cancer treatment in India.

(Images above of Dr. Indraneel Mittra and a representational photo of middle-aged women.)

Out of this humanitarian commitment emerged the now well-renowned Tata Memorial Hospital, commissioned by the Sir Dorabji Tata Trust on 28 February 1941.

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The San Francisco Police Department is proposing a new policy that would give robots the license to kill, as reported earlier by Mission Local (via Engadget). The draft policy, which outlines how the SFPD can use military-style weapons, states robots can be “used as a deadly force option when risk of loss of life to members of the public or officers is imminent and outweighs any other force option.”

As reported by Mission Local, members of the city’s Board of Supervisors Rules Committee have been reviewing the new equipment policy for several weeks. The original version of the draft didn’t include any language surrounding robots’ use of deadly force until Aaron Peskin, the Dean of the city’s Board of Supervisors, initially added that “robots shall not be used as a Use of Force against any person.”

However, the SFPD returned the draft with a red line crossing out Peskin’s addition, replacing it with the line that gives robots the authority to kill suspects. According to Mission Local, Peskin eventually decided to accept the change because “there could be scenarios where deployment of lethal force was the only option.” San Francisco’s rules committee unanimously approved a version of the draft last week, which will face the Board of Supervisors on November 29th.

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A new technique has been added to the CRISPR gene-editing toolbox. Known as PASTE, the system uses virus enzymes to “drag-and-drop” large sections of DNA into a genome, which could help treat a range of genetic diseases.

The CRISPR system originated in bacteria, which used it as a defense mechanism against viruses that prey on them. Essentially, if a bacterium survived a viral infection, it would use CRISPR enzymes to snip out a small segment of the virus DNA, and use that to remind itself how to fight off future infections of that virus.

Over the past few decades, scientists adapted this system into a powerful tool for genetic engineering. The CRISPR system consists of an enzyme, usually one called Cas9, which cuts DNA, and a short RNA sequence that guides the system to make this cut in the right section of the genome. This can be used to snip out problematic genes, such as those that cause disease, and can substitute them with other, more beneficial genes. The problem is that this process involves breaking both strands of DNA, which can be difficult for the cell to patch back up as intended, leading to unintended alterations and higher risks of cancer in edited cells.

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Scientists in Berlin have been studying a strange hereditary condition that causes half the people in certain families to have shockingly short fingers and abnormally high blood pressure for decades. If untreated, affected individuals often die of a stroke at the age of 50. Researchers at the Max Delbrück Center (MDC) in Berlin discovered the origin of the condition in 2015 and were able to verify it five years later using animal models: a mutation in the phosphodiesterase 3A gene (PDE3A) causes its encoded enzyme to become overactive, altering bone growth and causing blood vessel hyperplasia, resulting in high blood pressure.

“High blood pressure almost always leads to the heart becoming weaker,” says Dr. Enno Klußmann, head of the Anchored Signaling Lab at the Max Delbrück Center and a scientist at the German Centre for Cardiovascular Research (DZHK). As it has to pump against a higher pressure, Klußmann explains, the organ tries to strengthen its left ventricle. “But ultimately, this results in the thickening of the heart muscle – known as cardiac hypertrophy – which can lead to heart failure greatly decreasing its pumping capacity.”

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DNA can be utilized to reliably store massive amounts of digital data. However, it has hitherto proven challenging to retrieve or manipulate the specific data embedded in these molecules. Now, scientists from the CNRS and the University of Tokyo have developed the use of a novel enzyme-based technique, providing the initial clues as to how these technical obstacles may be overcome. Their research was recently published in the journal Nature.

Nature has unquestionably developed the best method for massive data storage: DNA. Based on this knowledge, DNA has been used to store digital data by translating binary (0 or 1) values into one of the four different DNA “letters” (A, T, C, or G).

But how can one search through the database of data encoded in DNA to discover a certain datum? And how is it possible to execute computations using DNA-encoded data without first transforming it into electronic form? These are the questions that research teams from the LIMMS (CNRS / University of Tokyo) and Gulliver (CNRS / ESPCI) laboratories have attempted to answer. They are experimenting with a new approach using enzymes and artificial neurons and neural networks for direct operations on DNA data.

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Blazars are some of the brightest objects in the cosmos. They are composed of a supermassive black hole.

A black hole is a place in space where the gravitational field is so strong that not even light can escape it. Astronomers classify black holes into three categories by size: miniature, stellar, and supermassive black holes. Miniature black holes could have a mass smaller than our Sun and supermassive black holes could have a mass equivalent to billions of our Sun.

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Learning-based computer-generated holography (CGH) has shown remarkable promise to enable real-time holographic displays. Supervised CGH requires creating a large-scale dataset with target images and corresponding holograms. We propose a diffraction model-informed neural network framework (self-holo) for 3D phase-only hologram generation. Due to the angular spectrum propagation being incorporated into the neural network, the self-holo can be trained in an unsupervised manner without the need of a labeled dataset. Utilizing the various representations of a 3D object and randomly reconstructing the hologram to one layer of a 3D object keeps the complexity of the self-holo independent of the number of depth layers. The self-holo takes amplitude and depth map images as input and synthesizes a 3D hologram or a 2D hologram. We demonstrate 3D reconstructions with a good 3D effect and the generalizability of self-holo in numerical and optical experiments.

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