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Circa 2018 immortality of the kidneys.

Kidney regeneration from pluripotent stem cells is receiving a lot of attention because limited treatments are currently available for chronic kidney disease (CKD). It has been shown that uremic state in CKD is toxic to somatic stem/progenitor cells, such as endothelial progenitor and mesenchymal stem cells, affecting their differentiation and angiogenic potential. Recent studies reported that specific abnormalities caused by the non-inherited disease are often retained in induced pluripotent stem cell (iPSC)-derived products obtained from patients. Thus, it is indispensable to first assess whether iPSCs derived from patients with CKD due to non-inherited disease (CKD-iPSCs) have the ability to generate kidneys.

Circa 2021 First breakthrough in immortality of the eyes of rats using the inducing of pluripotent stem cells in the eye. Which will eventually lead to immortality of the human eye.

The retina is neural tissue located in the posterior part of the eye and is an extension of the central nervous system (CNS), which has limited regenerative potential once damaged¹. Therefore, to maintain homeostasis of the retinal microenvironment and protect itself from harmful stimuli, the retina has a unique structure consisting of inner and outer blood-retinal barriers (BRBs)^2,3,4. The outer BRB is mainly composed of retinal pigment epithelial (RPE) cells, which support photoreceptor cells, the primary neurons in the retina, and play a significant role in the pathogenesis of retinal degenerative disorders, such as age-related macular degeneration (AMD) and retinitis pigmentosa (RP)^5,6,7,8,9. These disorders are commonly characterized by the irreversible loss of photoreceptor cells and RPE cells, and the only fundamental treatment for these retinal degenerative disorders is replacement of damaged or atrophied cells^10,11,12. Thus, regenerative treatments, such as stem cell transplantation, are emerging as attractive options for targeting retinal degeneration that was previously considered untreatable¹³.

RP refers to a set of hereditary retinal degenerative disorders that initially involve photoreceptors and leads to subsequent RPE cell damage; it affects 1 in 4,000 individuals worldwide⁹. Due to its inherent nature, extensive genetic studies are ongoing, and more than 50 causal genes have been identified¹⁴. Among the causal genes, PDE6B is a gene that encodes rod cGMP-phosphodiesterase, which is a critical component of the biochemical light transduction pathway⁹. Although various molecular and genetic studies have identified the pathomechanisms of RP, attempts to restore vision in patients with RP have failed. To overcome this issue, preclinical stem cell-based studies involving transient dosing or permanent implantation of pluripotent stem cells are being conducted^10,11,15,16.

Continue reading “Long-term effects of human induced pluripotent stem cell-derived retinal cell transplantation in Pde6b knockout rats” »

Whether or not a solid can emit light, for instance as a light-emitting diode (LED), depends on the energy levels of the electrons in its crystalline lattice. An international team of researchers led by University of Oldenburg physicists Dr. Hangyong Shan and Prof. Dr. Christian Schneider has succeeded in manipulating the energy-levels in an ultra-thin sample of the semiconductor tungsten diselenide in such a way that this material, which normally has a low luminescence yield, began to glow. The team has now published an article on its research in the science journal Nature Communications.

According to the researchers, their findings constitute a first step towards controlling the properties of matter through light fields. “The idea has been discussed for years, but had not yet been convincingly implemented,” said Schneider. The light effect could be used to optimize the optical properties of semiconductors and thus contribute to the development of innovative LEDs, solar cells, optical components and other applications. In particular the optical properties of organic semiconductors—plastics with semiconducting properties that are used in flexible displays and solar cells or as sensors in textiles—could be enhanced in this way.

Tungsten diselenide belongs to an unusual class of semiconductors consisting of a transition metal and one of the three elements sulfur, selenium or tellurium. For their experiments the researchers used a sample that consisted of a single crystalline layer of tungsten and selenium atoms with a sandwich-like structure. In physics, such materials, which are only a few atoms thick, are also known as two-dimensional (2D) materials. They often have unusual properties because the charge carriers they contain behave in a completely different manner to those in thicker solids and are sometimes referred to as “quantum materials.”

How to use causal influence diagrams to recognize the hidden incentives that shape an AI agent’s behavior.

There is rightfully a lot of concern about the fairness and safety of advanced Machine Learning systems. To attack the root of the problem, researchers can analyze the incentives posed by a learning algorithm using causal influence diagrams (CIDs). Among others, DeepMind Safety Research has written about their research on CIDs, and I have written before about how they can be used to avoid reward tampering. However, while there is some writing on the types of incentives that can be found using CIDs, I haven’t seen a succinct write up of the graphical criteria used to identify such incentives. To fill this gap, this post will summarize the incentive concepts and their corresponding graphical criteria, which were originally defined in the paper Agent Incentives: A Causal Perspective.

A causal influence diagram is a directed acyclic graph where different types of nodes represent different elements of an optimization problem. Decision nodes represent values that an agent can influence, utility nodes represent the optimization objective, and structural nodes (also called change nodes) represent the remaining variables such as the state. The arrows show how the nodes are causally related with dotted arrows indicating the information that an agent uses to make a decision. Below is the CID of a Markov Decision Process, with decision nodes in blue and utility nodes in yellow:

Continue reading “Spotting Unfair or Unsafe AI using Graphical Criteria” »

The liver appears to play a significant role in regulating feeding behaviors in mice.

A new generation of scientists is growing up with CRISPR technology. Here’s how some high school students learn to edit genes.

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