Neuroscience

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Curtis Cripe

Neuroscience https://thejournal.com/~/media/EDU/THEJournal/2018%20Images/20180625neuroscience.jpg

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Normal Cell Metabolism: Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function. Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released. Cell utilize energy in the form of ATP. The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process. Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP. The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos). It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation. If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use. IN CANCER CELL: Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful. Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation. This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive. Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful. Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation. This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive. introduction to WARBERG PHENOMENA: WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside. Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme. WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.

Cancer cell metabolism: special Reference to Lactate Pathway

AADYARAJPANDEY1

The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed. In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine. Bio Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.

FAIR & AI Ready KGs for Explainable Predictions

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Hadean zircons provide a potential record of Earth's earliest subduction 4.3 billion years ago. Itremains enigmatic how subduction could be initiated so soon after the presumably Moon‐forming giant impact(MGI). Earlier studies found an increase in Earth's core‐mantle boundary (CMB) temperature due to theaccumulation of the impactor's core, and our recent work shows Earth's lower mantle remains largely solid, withsome of the impactor's mantle potentially surviving as the large low‐shear velocity provinces (LLSVPs). Here,we show that a hot post‐impact CMB drives the initiation of strong mantle plumes that can induce subductioninitiation ∼200 Myr after the MGI. 2D and 3D thermomechanical computations show that a high CMBtemperature is the primary factor triggering early subduction, with enrichment of heat‐producing elements inLLSVPs as another potential factor. The models link the earliest subduction to the MGI with implications forunderstanding the diverse tectonic regimes of rocky planets.

A Giant Impact Origin for the First Subduction on Earth

Sérgio Sacani

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Below are the important points I note from the 2020 paper by Martin Grohe: - 1-WL distinguishes almost all graphs, in a probabilistic sense - Classical WL is two dimensional Weisfeiler-Leman - DeepWL is an unlimited version of WL graph that runs in polynomial time. - Knowledge graphs are essentially graphs with vertex/edge attributes ABSTRACT: Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view. Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.

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Subhajit Sahu

Cyanobacteria (also known as blue-green algae) are ubiquitous photosynthetic microorganisms found in diverse habitats such as fresh water, marine water, moist rocks, etc. The photosynthetic mode of nutrition makes them significant global oxygen producers along with nitrogen-fixing ability of heterocyst and carbon sequestration. Some cyanobacterial species have the ability to perform a dual mode of nutritional procurement. This unique capability of cyanobacteria to utilize both organic (heterotrophic) and inorganic (autotrophic) carbon sources for energy production and growth is termed as mixotrophy which impart nutritional flexibility and competitive ability to them. Cyanobacterial mixotrophy provides the promising avenues in biotechnological applications such as wastewater treatment, bioremediation, pharmaceuticals, food supplements, biofertilizer, coloring agents, synthesis of bioactive compounds and as an agent for eco-friendly bio-fuels generation, etc Mixotrophically grown cyanobacteria, demonstrate significant potential for efficient and economical applications beyond their conventional agricultural application, thereby offering a versatile and impactful resource for future technological and environmental challenges.

mixotrophy in cyanobacteria: a dual nutritional strategy

MansiBishnoi1

Mammals have dominated Earth for approximately 55 Myr thanks to their adaptations and resilience to warming and cooling during the Cenozoic. All life will eventually perish in a runaway greenhouse once absorbed solar radiation exceeds the emission of thermal radiation in several billions of years. However, conditions rendering the Earth naturally inhospitable to mammals may develop sooner because of long-term processes linked to plate tectonics (short-term perturbations are not considered here). In ~250 Myr, all continents will converge to form Earth’s next supercontinent, Pangea Ultima. A natural consequence of the creation and decay of Pangea Ultima will be extremes in pCO2 due to changes in volcanic rifting and outgassing. Here we show that increased pCO2, solar energy (F⨀; approximately +2.5% W m−2 greater than today) and continentality (larger range in temperatures away from the ocean) lead to increasing warming hostile to mammalian life. We assess their impact on mammalian physiological limits (dry bulb, wet bulb and Humidex heat stress indicators) as well as a planetary habitability index. Given mammals’ continued survival, predicted background pCO2 levels of 410–816 ppm combined with increased F⨀ will probably lead to a climate tipping point and their mass extinction. The results also highlight how global landmass configuration, pCO2 and F⨀ play a critical role in planetary habitability.

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The magnetic dynamo cycle of the Sun features a distinct pattern: a propagating region of sunspot emergence appears around 30° latitude and vanishes near the equator every 11 years (ref. 1). Moreover, longitudinal flows called torsional oscillations closely shadow sunspot migration, undoubtedly sharing a common cause2. Contrary to theories suggesting deep origins of these phenomena, helioseismology pinpoints low-latitude torsional oscillations to the outer 5–10% of the Sun, the near-surface shear layer3,4. Within this zone, inwardly increasing differential rotation coupled with a poloidal magnetic field strongly implicates the magneto-rotational instability5,6, prominent in accretion-disk theory and observed in laboratory experiments7. Together, these two facts prompt the general question: whether the solar dynamo is possibly a near-surface instability. Here we report strong affirmative evidence in stark contrast to traditional models8 focusing on the deeper tachocline. Simple analytic estimates show that the near-surface magneto-rotational instability better explains the spatiotemporal scales of the torsional oscillations and inferred subsurface magnetic field amplitudes9. State-of-the-art numerical simulations corroborate these estimates and reproduce hemispherical magnetic current helicity laws10. The dynamo resulting from a well-understood near-surface phenomenon improves prospects for accurate predictions of full magnetic cycles and space weather, affecting the electromagnetic infrastructure of Earth.

The solar dynamo begins near the surface

Sérgio Sacani

In this work, we assess the potential detectability of solar panels made of silicon on an Earth-like exoplanet as a potential technosignature. Silicon-based photovoltaic cells have high reflectance in the UV-VIS and in the near-IR, within the wavelength range of a space-based flagship mission concept like the Habitable Worlds Observatory (HWO). Assuming that only solar energy is used to provide the 2022 human energy needs with a land cover of ∼ 2.4%, and projecting the future energy demand assuming various growth-rate scenarios, we assess the detectability with an 8 m HWO-like telescope. Assuming the most favorable viewing orientation, and focusing on the strong absorption edge in the ultraviolet-to-visible (0.34 − 0.52 µm), we find that several 100s of hours of observation time is needed to reach a SNR of 5 for an Earth-like planet around a Sun-like star at 10pc, even with a solar panel coverage of ∼ 23% land coverage of a future Earth. We discuss the necessity of concepts like Kardeshev Type I/II civilizations and Dyson spheres, which would aim to harness vast amounts of energy. Even with much larger populations than today, the total energy use of human civilization would be orders of magnitude below the threshold for causing direct thermal heating or reaching the scale of a Kardashev Type I civilization. Any extraterrrestrial civilization that likewise achieves sustainable population levels may also find a limit on its need to expand, which suggests that a galaxy-spanning civilization as imagined in the Fermi paradox may not exist.

Detectability of Solar Panels as a Technosignature

Sérgio Sacani

We report on the discovery of Gliese 12 b, the nearest transiting temperate, Earth-sized planet found to date. Gliese 12 is a bright (V = 12.6 mag, K = 7.8 mag) metal-poor M4V star only 12.162 ± 0.005 pc away from the Solar system with one of the lowest stellar activity levels known for M-dwarfs. A planet candidate was detected by TESS based on only 3 transits in sectors 42, 43, and 57, with an ambiguity in the orbital period due to observational gaps. We performed follow-up transit observations with CHEOPS and ground-based photometry with MINERVA-Australis, SPECULOOS, and Purple Mountain Observatory, as well as further TESS observations in sector 70. We statistically validate Gliese 12 b as a planet with an orbital period of 12.76144 ± 0.00006 d and a radius of 1.0 ± 0.1 R⊕, resulting in an equilibrium temperature of ∼315 K. Gliese 12 b has excellent future prospects for precise mass measurement, which may inform how planetary internal structure is affected by the stellar compositional environment. Gliese 12 b also represents one of the best targets to study whether Earth-like planets orbiting cool stars can retain their atmospheres, a crucial step to advance our understanding of habitability on Earth and across the galaxy.

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Sérgio Sacani

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