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With NASA’s Artemis 1 mission launching to the moon this month, Space.com is taking a look at what we know about the moon and why we care. Join us for our Moon Week special report in the countdown to Artemis 1.

Lunar exploration is often described, even in the moment, with the lofty language of history books and the achievements of humanity. And with good reason — each new mission to the moon presents the possibility of ground-breaking discovery and historic firsts. But without care, lunar missions could also endanger the historic sites of prior human exploration.

A new molecule created by a researcher at the University of Texas at Dallas kills a variety of difficult-to-treat cancers, including triple-negative breast cancer, by taking advantage of a weakness in cells that was not previously targeted by existing drugs.

The research, which was conducted using isolated cells, human cancer tissue, and mouse-grown human cancers, was recently published in Nature Cancer.

A co-corresponding author of the study and an associate professor of chemistry and biochemistry in the School of Natural Sciences and Mathematics at the University of Texas at Dallas, Dr. Jung-Mo Ahn has dedicated more than ten years of his career to developing small molecules that target protein-protein interactions in cells. He previously created potential therapeutic candidate compounds for treatment-resistant prostate cancer and breast cancer using a method called structure-based rational drug design.

New green-light absorbing photodetectors could be useful for medical sensors, fingerprint recognition, and more.

New green-light absorbing transparent organic photodetectors that are highly sensitive and compatible with CMOS fabrication methods have been developed and demonstrated by researchers. Incorporating these new photodetectors into organic-silicon hybrid image sensors could be useful for many applications. These include light-based heart-rate monitoring, fingerprint recognition, and devices that detect the presence of nearby objects.

Whether used in scientific cameras or smartphones, most of today’s imaging sensors are based on CMOS technology and inorganic photodetectors that convert light signals into electric signals. Although photodetectors made from organic materials are attracting attention because they can help boost sensitivity, for example, it has thus far proven difficult to fabricate high-performance organic photodetectors.

Using electrocorticography (ECoG), we probed the neurocognitive substrates of mentalizing at the level of neuronal populations. We found that mentalizing about the self and others recruited near-identical cortical sites (Fig. 5a, b) in a common spatiotemporal sequence (Figs. 5 c and 6). Within our ROIs, activations began in visual cortex, followed by temporoparietal DMN regions (TPJ, ATL, and PMC), and lastly in mPFC regions (amPFC, dmPFC, and vmPFC; Fig. 3e, f). Critically, regions with later activations exhibited greater functional specialization for mentalizing as measured by three metrics: functional specificity for mentalizing versus arithmetic (Figs. 3 c, d and 4b), self/other differentiation in activation timing (Fig. 5c, d), and temporal associations with behavioral responses (Fig. 4D and Table 1). Taken together, these results reveal a common neurocognitive sequence28,29,30,31 for self-and other-mentalizing, beginning in visual cortex (low specialization), ascending through temporoparietal DMN areas (intermediate specialization), then reaching its apex in mPFC regions (high specialization).

Our results are consistent with gradient-based models of brain function, which posit that concrete sensorimotor processing in unimodal regions (e.g., visual cortex) gradually yields to increasingly abstract and inferential processing in ‘high-level’ transmodal regions like mPFC41,42. We found that the strength of self/other differences in activation timing increased along a gradient from visual cortex to mPFC. Specifically, other-mentalizing evoked slower (Fig. 5c) and lengthier (Fig. 5d) activations than self-mentalizing throughout successive DMN ROIs. These self/other functional differences corresponded with self/other differences in RTBehav (Supplementary Fig. 4), although the two were often dissociable (Table 1). Thus, perhaps because we know ourselves better than others, other-mentalizing may require lengthier processing at more abstract and inferential levels of representation, ultimately resulting in slower behavioral responses.

What might our results imply about extant fMRI findings? Hundreds of fMRI studies consistently suggest that: TPJ and dmPFC are most crucial for mentalizing6,8,11,12,43,44,45,46, and dmPFC is selective for thinking about others over oneself 32,33,34,35. However, when examined with ECoG, we found that both pieces of received wisdom are not what they seem. Below, we discuss both issues before moving onto our peculiar vmPFC results, and then conclude with systems-level discussion.

Analysis of the genome and proteome shows that eukaryotic evolution gave rise to the regulatory function of chromatin.

Two meters of DNA

DNA, or deoxyribonucleic acid, is a molecule composed of two long strands of nucleotides that coil around each other to form a double helix. It is the hereditary material in humans and almost all other organisms that carries genetic instructions for development, functioning, growth, and reproduction. Nearly every cell in a person’s body has the same DNA. Most DNA is located in the cell nucleus (where it is called nuclear DNA), but a small amount of DNA can also be found in the mitochondria (where it is called mitochondrial DNA or mtDNA).

Although quantum computing is not commercially available, CISA (Cybersecurity and Infrastructure Security Agency) urges organizations to prepare for the dawn of this new age, which is expected to bring groundbreaking changes in cryptography, and how we protect our secrets.

The agency published a paper earlier in the week, calling for leaders to start preparing for the migration to stronger secret guarding systems, exploring risk mitigation methods, and participating in developing new standards.

Summary: A newly developed artificial intelligence model can detect Parkinson’s disease by reading a person’s breathing patterns. The algorithm can also discern the severity of Parkinson’s disease and track progression over time.

Source: MIT

Parkinson’s disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset.