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Just in time for the holidays, NASA’s James Webb Space Telescope (JWST) recently used its Near-Infrared (NIRCam) instrument to capture stunning images of the massive supernova remnant, Cassiopeia A (Cas A), comes after JWST used its Mid-Infrared Instrument (MIRI) to capture its own images of Cas A earlier this year. Along with being comprised of different colors, each image provides different details of Cas A, with some features being visible in one image that aren’t visible in the other image. In either case, this most recent NIRCam image continues to offer stunning insights into one of the most well-known supernova remnants that spans 10 light-years in diameter and located approximately 11,000 light-years from Earth.

Recent image of the supernova remnant, Cassiopeia A (Cas A), taken by NASA’s James Webb Space Telescope, revealing details like never before. (Credit: NASA, ESA, CSA, STScI, D. Milisavljevic (Purdue University), T. Temim (Princeton University), I. De Looze (University of Gent))

“With NIRCam’s resolution, we can now see how the dying star absolutely shattered when it exploded, leaving filaments akin to tiny shards of glass behind,” said Dr. Danny Milisavljevic, who is an Associate Professor of Physics and Astronomy ay Purdue University and is the research team lead. “It’s really unbelievable after all these years studying Cas A to now resolve those details, which are providing us with transformational insight into how this star exploded.”

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A critical severity vulnerability in a WordPress plugin with more than 90,000 installs can let attackers gain remote code execution to fully compromise vulnerable websites.

Known as Backup Migration, the plugin helps admins automate site backups to local storage or a Google Drive account.

The security bug (tracked as CVE-2023–6553 and rated with a 9.8÷10 severity score) was discovered by a team of bug hunters known as Nex Team, who reported it to WordPress security firm Wordfence under a recently launched bug bounty program.

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Conditioning the lungs with interferon-gamma, a natural immune system protein (cytokine) best known for fighting bacterial infections, appears to be a strong antiviral for SARS-CoV-2, according to National Institutes of Health scientists and colleagues. Their new study, published in Nature Communications, shows in two different mouse models that when a bacterial infection triggers the release of interferon-gamma in the lungs, those animals subsequently are protected from infection by SARS-CoV-2, the virus that causes COVID-19. The investigators further report that using recombinant interferon-gamma in the nose of study mice at the time of viral exposure substantially reduces SARS-CoV-2 infection and COVID disease.

The lead project scientists suggest testing interferon-gamma further, alone and in combination with other treatments, to limit early SARS-CoV-2 infection in people. They also hypothesize that people with prior bacterial infections that naturally release interferon-gamma in their lungs may be less susceptible to COVID-19.

NIH’s National Institute of Allergy and Infectious Diseases (NIAID) led the project with collaborators at Malaghan Institute of Medical Research in New Zealand.

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😀 Amazing breakthrough face_with_colon_three

A group of Spanish researchers have developed a brain-computer interface based on electroencephalograms that allowed a group of 22 users to play a simple multiplayer game. The interface was 94% accurate in translating players’ thoughts into game moves, with each move taking just over 5 seconds. The study was published in Frontiers in Human Neuroscience.

A brain-computer interface is a technology that enables direct communication between the human brain and external devices, such as computers or prosthetic limbs. Brain-computer interfaces work by detecting and interpreting neural signals, typically through electrodes placed on the user’s head. These signals are then translated into actionable commands, allowing individuals to control computers, devices, or applications using their thoughts.

Brain-computer interfaces offer significant potential in medicine, from helping paralyzed individuals regain environmental control to treating neurological disorders. However, their broader adoption is hindered by challenges in accuracy and the extended time required to interpret brain signals.

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Introduction and objective: Video games are crucial to the entertainment industry, nonetheless they can be challenging to access for those with severe motor disabilities. Brain-computer interfaces (BCI) systems have the potential to help these individuals by allowing them to control video games using their brain signals. Furthermore, multiplayer BCI-based video games may provide valuable insights into how competitiveness or motivation affects the control of these interfaces. Despite the recent advancement in the development of code-modulated visual evoked potentials (c-VEPs) as control signals for high-performance BCIs, to the best of our knowledge, no studies have been conducted to develop a BCI-driven video game utilizing c-VEPs. However, c-VEPs could enhance user experience as an alternative method. Thus, the main goal of this work was to design, develop, and evaluate a version of the well-known ‘Connect 4’ video game using a c-VEP-based BCI, allowing 2 users to compete by aligning 4 same-colored coins vertically, horizontally or diagonally.

Methods: The proposed application consists of a multiplayer video game controlled by a real-time BCI system processing 2 electroencephalograms (EEGs) sequentially. To detect user intention, columns in which the coin can be placed was encoded with shifted versions of a pseudorandom binary code, following a traditional circular shifting c-VEP paradigm. To analyze the usability of our application, the experimental protocol comprised an evaluation session by 22 healthy users. Firstly, each user had to perform individual tasks. Afterward, users were matched and the application was used in competitive mode. This was done to assess the accuracy and speed of selection. On the other hand, qualitative data on satisfaction and usability were collected through questionnaires.

Results: The average accuracy achieved was 93.74% ± 1.71%, using 5.25 seconds per selection. The questionnaires showed that users felt a minimal workload. Likewise, high satisfaction values were obtained, highlighting that the application was intuitive and responds quickly and smoothly.

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The experiment mirrored the principles of the quantum bomb tester, where a photon’s wave-particle behavior was theorized to detect the presence of a bomb without directly interacting with it.

A new study demonstrated how a droplet’s behavior imitates certain behaviors predicted for quantum particles — particularly photons.

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