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Driving Toward the Elimination of Cancer — Joel Greshock — VP, Oncology, Data Science & Digital Health, Janssen Pharmaceutical Companies of Johnson & Johnson.


Joel Greshock is VP, Oncology, Data Science & Digital Health, Janssen Research & Development (https://www.janssen.com/oncology/leadership-team). In this position, he is responsible for creating unique and actionable medical insights using large and increasingly available datasets. The focus of this research includes discovering novel therapeutic targets, identifying areas of unmet medical need, and enhancing clinical trial recruitment and execution.

Prior to joining Janssen R&D, Joel served as Vice President of Bioinformatics at Neon Therapeutics, Inc., where he built and managed the Data Sciences organization. At Neon, he was responsible for the design and deployment of personalized cancer therapies now under clinical evaluation.

Prior to joining Neon, Joel served as Head of Oncology Translational Informatics for Novartis, where he was responsible for the correlation of patient outcomes with molecular biomarkers, identification of mechanisms of clinical resistance and computational research for assets approaching or being evaluated in early phases of development.

Before joining Novartis, Joel assumed numerous roles for GlaxoSmithKline Oncology, which included Head of Bioinformatics. Earlier in his career, Joel was a Data Analyst at Abramson Family Cancer Research Institute at the University of Pennsylvania, where he built early generation microarray platforms and developed widely used predictive models for cancer predisposition mutations.

This post is also available in: he עברית (Hebrew)

According to a report done by Surfshark VPN, out of the approximately 5 billion of internet users, over 1.6 billion of them (31% of users) use a VPN. That’s close to a fifth of the worlds population.

A VPN, or a Virtual Private Network, is a mechanism for creating a secure connection between a computing device and a computer network, or between two networks, using an insecure communication medium such as the public Internet. A VPN can extend a private network (one that disallows or restricts public access), enabling users to send and receive data across public networks as if their devices were directly connected to the private network.

Shortform link:
https://shortform.com/artem.

In this video we will explore a very interesting paper published in Nature in 2022, which describes the hidden torus in the neuronal activity of cells in the entorhinal cortex, known as grid cells.

Place cell video: https://www.youtube.com/watch?v=iV-EMA5g288&t=158s.

Neural manifolds video: https://www.youtube.com/watch?v=QHj9uVmwA_0

My name is Artem, I’m a computational neuroscience student and researcher.
Socials:
Twitter: https://twitter.com/ArtemKRSV

REFERENCES:

Shortform link:
https://shortform.com/artem.

My name is Artem, I’m a computational neuroscience student and researcher.

In this video we will talk about the fundamental role of lognormal distribution in neuroscience. First, we will derive it through Central Limit Theorem, and then explore how it support brain operations on many scales — from cells to perception.

REFERENCES:

1. Buzsáki, G. & Mizuseki, K. The log-dynamic brain: how skewed distributions affect network operations. Nat Rev Neurosci 15264–278 (2014).
2. Ikegaya, Y. et al. Interpyramid Spike Transmission Stabilizes the Sparseness of Recurrent Network Activity. Cerebral Cortex 23293–304 (2013).
3. Loewenstein, Y., Kuras, A. & Rumpel, S. Multiplicative Dynamics Underlie the Emergence of the Log-Normal Distribution of Spine Sizes in the Neocortex In Vivo. Journal of Neuroscience 31, 9481–9488 (2011).
4. Morales-Gregorio, A., van Meegen, A. & van Albada, S. J. Ubiquitous lognormal distribution of neuron densities across mammalian cerebral cortex. http://biorxiv.org/lookup/doi/10.1101/2022.03.17.480842 (2022) doi:10.1101/2022.03.17.480842.

OUTLINE:

Head to https://linode.com/scishow to get a $100 60-day credit on a new Linode account. Linode offers simple, affordable, and accessible Linux cloud solutions and services.

Scientists like to measure things, but they’ve had a heck of a time doing that with sharpness. And even if no one agrees on exactly how to measure it, our search for better tools has recently led to some of the sharpest objects we’ve ever created.

Hosted by: Hank Green (he/him)
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Sources:
https://www.tf.uni-kiel.de/matwis/amat/iss/kap_c/backbone/rc_2_4.html.
https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1524-4725.1982.tb01093.x.
https://www.proquest.com/openview/ecab7dfbc6c1cfec6fabf4d0f7…e=gscholar.
https://www.sciencedirect.com/science/article/pii/S0003687006000238
https://physics.aps.org/articles/v9/155
https://www.guinnessworldrecords.com/world-records/sharpest-object-man-made.
https://books.google.com/books?id=gDflDwAAQBAJ&pg=PA71&lpg=P…le&f=false.
https://pubmed.ncbi.nlm.nih.gov/12124714/
https://www.jstor.org/stable/44159720?read-now=1#page_scan_tab_contents.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3380774/
https://www.sciencefocus.com/science/whats-the-sharpest-knife-in-the-world/

scienceofsharp


https://www.sciencedirect.com/science/article/pii/S0924013604007022
https://www.sciencedirect.com/science/article/pii/S0013794406004073
https://www.scientific.net/KEM.293-294.769
https://www.sciencedirect.com/science/article/pii/S0020740318322665
https://www.cambridge.org/core/journals/robotica/article/abs…7BAFBA8B6F
https://link.springer.com/content/pdf/10.1007/s10816-022…;0.pdf?pdf.

Images:
https://commons.wikimedia.org/wiki/File: Crater_knife_edge.jpg.

Dual Grit Sharpening


https://www.researchgate.net/figure/Artificial-sapphire-scal…_354874023
https://www.southampton.ac.uk/biu/galleries/sem.page.
https://commons.wikimedia.org/wiki/File: Obsidian_blade_mounted_in_ornamental_handle,_from_Admiralty_Wellcome_M0015133.jpg.
https://commons.wikimedia.org/wiki/File:Blade_MET_VS1994_35_468.jpeg.
https://commons.wikimedia.org/wiki/File:Macro_sewing_machine_needles.jpg.
https://commons.wikimedia.org/wiki/File:Beveled_tip_of_a_hyp…14_005.JPG
https://www.researchgate.net/figure/SEM-image-of-a-carbon-na…2_51450738
https://commons.wikimedia.org/wiki/File:Scanning_Tunneling_M…ematic.svg.
https://commons.wikimedia.org/wiki/File:%D0%9E%D0%B4%D0%BD%D…%D0%B0.jpg.
https://commons.wikimedia.org/wiki/File:Prehistoric_Denmark_…51010).jpg

The breakthrough experiment could lead to low-energy, wave-based computers and new applications for wireless communications.

Researchers at the Advanced Science Research Center at the CUNY Graduate Center (CUNY ASRC) performed a breakthrough experiment in which they observed time reflections of electromagnetic signals in a tailored metamaterial.

Time reflection versus spatial reflection.


Andrea Alu.

The scientists, who published their findings in a paper in Nature Physics, were able to successfully cause time reversal as well as frequency conversion of broadband electromagnetic waves in their experiments.

An international team of researchers, including those from the University of Tokyo’s Institute for Solid State Physics, has made a groundbreaking discovery. They have successfully demonstrated the use of a single molecule named fullerene as a switch, similar to a transistor. The team achieved this by employing a precisely calibrated laser pulse, which allowed them to control the path of an incoming electron in a predictable manner.

The switching process enabled by fullerene molecules can be significantly faster than the switches used in microchips, with a speed increase of three to six orders of magnitude, depending on the laser pulses utilized. The use of fullerene switches in a network could result in the creation of a computer with capabilities beyond what is currently achievable with electronic transistors. Additionally, they have the potential to revolutionize microscopic imaging devices by providing unprecedented levels of resolution.

Over 70 years ago, physicists discovered that molecules emit electrons in the presence of electric fields, and later on, certain wavelengths of light. The electron emissions created patterns that enticed curiosity but eluded explanation. But this has changed thanks to a new theoretical analysis, the ramification of which could not only lead to new high-tech applications but also improve our ability to scrutinize the physical world itself.

Reports say that the Nokia Magic Max will come in three different memory configurations. We will have 8GB, 12GB and 16GB of RAM with 256GB and 512GB storage options. It will launch with Android 13 out of the box with Snapdragon 8 Gen 2 SoC under the hood. We may also see a 6.7-inch AMOLED display with 120Hz refresh rate on the device. Corning Gorilla Glass 7 protection could be on the display of the upcoming flagship device from Nokia.

The device will feature a triple camera setup on the back with 144MP main sensor, 64MP ultrawide and 48MP Telephoto lens. Rumors have suggested a massive 7950mAh battery which can also charge from 0 to 100 within a few minutes, thanks to the 180W fast charger.

The memory configurations will determine the price of each variant. Nevertheless, sources have suggested the starting price to be around $550 (INR44,900). There is no firm rumor with respect to the launch date, but we expect to see the launch of the Nokia Magic Max in a matter of few weeks.

The industry is gaining ground in understanding how aging affects reliability, but more variables make it harder to fix.

Circuit aging is emerging as a first-order design challenge as engineering teams look for new ways to improve reliability and ensure the functionality of chips throughout their expected lifetimes.

The need for reliability is obvious in data centers and automobiles, where a chip failure could result in downtime or injury. It also is increasingly important in mobile and consumer electronics, which are being used for applications such as in-home health monitoring or for navigation, and where the cost of the devices has been steadily rising. But aging also needs to be assessed in the context of variation models from the foundries, different use cases that may stress various components in different ways, and different power and thermal profiles, all of which makes it harder to accurately predict how a chip will behave over time.