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Displaying entries 31 through 45 of 69 found.

Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet Incubator Lifescape

Yang, Sheng, et al. The stability of unevenly spaced planetary systems. arXiv:2308.16798. We first post this paper about solar system by seven astrophysicists across China so that it is online. A longer review will follow.

Studying the orbital stability of multi-planet systems is essential to understand planet formation, estimate the stable time of an observed planetary system, and advance population synthesis models. Although previous studies have primarily focused on ideal systems characterized by uniform orbital separations, in reality a diverse range of orbital separations exists among planets within the same system. This study focuses on investigating the dynamical stability of systems with non-uniform separation. We considered a system with 10 planets with masses of 10−7 solar masses around a central star with a mass of 1 solar mass. We conclude that when estimating the orbital crossing time and colliding pairs in a realistic situation, updating the formula derived for evenly spaced systems would be necessary. (Excerpt)

The N-body simulation results are as follows:
• Orbit crossing times become shorter in the case of pairs with shorter initial orbital separations.
• There is a correlation between the closest separation pair and the first close encounter or collision.
• The orbital crossing time for systems with uneven orbital separation could be expressed based on the orbital periods of the closest separation pair. (5)

Balbi, Amedeo and Adam Frank. The Oxygen Bottleneck for Technospheres. arXiv:2308.01160. After some 15 years of habitable exoworld studies across solar system and galactic zones, University of Rome and University of Rochester astrophysicists (search each) realize that the narrow O2 window between enough for fauna to breathe but not for flora to combust is a prime parameter with regard to possible intelligent civilizations. At this juncture, one more
strident value now delimits the presence of optimum Earth-like occasions. So still another winnowing factor might well distinguish our fittest anthropocene to astropocene opportunity.

On Earth, the development of technology required easy access to open air combustion, which is only possible when oxygen partial pressure, P(O2), is above 18\%. This suggests that only planets with significant atmospheric oxygen concentrations will be capable of developing advanced technospheres.

Fig. 1: Planets capable of supporting high O2 concentrations and, hence, technological
civilizations. This figure shows the likely composition of atmospheres based on mass
and equilibrium temperature. For planets whose temperature and mass would lead to
CO2/N2/H2O atmospheres, high oxygen levels require a biological origin, i.e. photosynthesis.

Fig. 2: Earth’s atmospheric O2 concentration over time. Top: O2 concentration at 4 Gyr ago. Bottom: O2 concentrations 600 Myr ago, around the development of multicellular life. The horizontal lines represent the current P(O2) level, the threshold for global wildfire and the minimum requirement for combustion. In both figures we label important events in the evolution of life.

Hoang, John, et al. Exploring the Use of Generative AI in the Search for Extraterrestrial Intelligence (SETI). arXiv:2308.13125. In the context of the Breakthrough Listen project, Yale, Ohio State, Toronto, and UC Berkeley computer scientists propose to integrate and advance prior surveillance methods with deep language learning with capabilities.

The search for extraterrestrial intelligence (SETI) is a field that has long been within the domain of traditional signal processing techniques. However, with the advent of powerful generative AI models, such as GPT-3, we are now able to explore new ways of analyzing SETI data and potentially uncover previously hidden signals. In this work, we present a novel approach for using generative AI to analyze SETI data, with focus on data processing and machine learning techniques. Our proposed method uses a combination of deep learning and generative models to analyze radio telescope data, with the goal of identifying potential signals from extraterrestrial civilizations.

Joirot, Sarah. A race against the clock: Constraining the timing of cometary bombardment relative to Earth's growth. arXiv:2309.03954. Into later 2923 seven astroscientists from the University of Bordeaux, University of Paris, Johns Hopkins and Southwest Research Institute including Sean Raymond realize and quantify one more precarious parameter for life's occasion in the degree, composition and temporal frequency as vital components may bath these ancient phases.

Comets are a potential source of inner solar system volatiles, but the timing of this delivery relative to Earth's accretion is poorly understood. Here, we evaluate whether dynamical simulations in the context of an Early Instability model. We perform dynamical simulations of the solar system, calculate the probability of collision between comets and Earth analogs component embryos through time and estimate the total cometary mass accreted in Earth analogs as a function of time. While our results agree with geochemistry, we also show that the contribution of comets might have been delayed with by the stochastic timing of an influx. These results emphasize the variable nature of the primordial solar system. (Excerpt)

Kasting, James. The Goldilocks Planet? How Silicate Weathering Maintains Earth “Just Right”. Elements. 15/4, 2019. Two decades into the 21st century, the senior Penn State University geoscientist is can now reconstruct the past history of Earth's variable chemical composition so to realize that this surface condition might be most suitable for life to uniquely appear, evolve and persist

Earth's climate is buffered over long timescales by a negative feedback between atmospheric CO2 level and surface temperature. The rate of silicate weathering slows as the climate cools, causing CO2 to increase and warming the surface through the greenhouse effect. This buffering system has kept liquid water stable at Earth's surface. Most silicate weathering is thought to occur on the continents today, but seafloor weathering may have been equally important.

Ecosmomics: An Independent, UniVersal, Source Code-Script of Generative Complex Network Systems

Cajic, Pavle, et al. On the information-theoretic formulation of network participation. arXiv:2307.12556. University of Sydney systems theorists including Joseph Lizier describe an improved finesse of a method by which to parse relative multiples meanings.

The participation coefficient is a measure of the diversity of a node's connections with respect to a modular partition. While diversity metrics have been studied in other fields such as ecology, they have not been applied to networks. Here we show that the distinction is an approximation to participation entropy and use the additive properties of entropy to develop new metrics of connection diversity. Our information-theoretic formalism developed allows new and more subtle connection patterns in complex networks to be studied.

Landry, Nicholas, et al. The simpliciality of higher-order networks. arXiv:2308.13918. University of Vermont and Grinnell College system theorists heighten our understandings as nature's vital connectivities ever expand and deepen. See also Topology and dynamics of higher-order multiplex networks by Sanjukta Krishnagopal and Ginestra Bianconi at arXiv:2308.14189.
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Higher-order networks are widely used to describe complex systems in which interactions can involve more than two entities at once. In this paper, we focus on inclusion within higher-order networks, referring to situations where specific entities participate in an interaction, and subsets of those entities also interact with each other. Traditional modeling approaches to higher-order networks tend to either not consider inclusion at all (e.g., hypergraph models) or explicitly assume perfect and complete inclusion (e.g., simplicial complex models). To allow for a more nuanced assessment of inclusion in higher-order networks, we introduce the concept of "simpliciality" and several corresponding measures. Contrary to current modeling practice, we show that empirically observed systems rarely lie at either end of the simpliciality spectrum. (Abstract)

A wide range of complex systems are shaped by interactions involving several entities at once: social networks are driven by group behavior [1], emails often have multiple recipients [2–4], molecular pathways in cells involve multi-protein interactions [5], and scientific articles in-
volve groups of co-authors [6]. Higher-order networks are a natural extension to networks explicitly designed to model such multiway relationships [7]

Samsel, Mateusz, et al. Towards fractal origins of the community structure in complex networks. arXiv:2309.11126.. In this year Warsaw University of Technology theorists advance further ways to perceive an inherent self-similarity across multiplex phenomena.

In this paper, we pose a hypothesis that the structure of communities in complex networks may result from their latent fractal properties. Quantitative arguments supporting this hypothesis are that many non-fractal real complex networks that have a well-defined community structure reveal fractal properties and the scale-free community size distributions observed in many real networks directly relates to scale-invariant box mass distributions. A fractal core can be identified as a macroscopic component when the edges between modules identified by the community detection algorithm. (Excerpt)

Wellnitz, David, et al. A Network Approach to Atomic Spectra.. Journal of Physics: Complexity. July, 2023. University of Strasbourg, Heidelberg and Konstanz, along with MPI Intelligent Systems researchers report that even these quantum material depths can be found to exhibit and hold to nature's common node/link netwise dynamic animations.

Network science provides a universal framework for modeling complex systems, contrasting the reductionist approach generally adopted in physics. In a prototypical study, we utilize network models created from spectroscopic data of atoms to predict microscopic properties of the underlying physical system. For simple atoms such as helium, an a posteriori inspection of spectroscopic network communities reveals the emergence of quantum numbers and symmetries. For more complex atoms such as thorium, finer network hierarchies suggest additional microscopic symmetries or configurations. Our work promotes a genuine bi-directional exchange of methodology between network science and physics, and presents new perspectives for the study of atomic spectra.

Zitnik, Marinka, et al.. Current and future directions in network biology. arXiv:2309.08478. Thirty-six scientists from across the USA and onto France, the UK and Brazil met last year to broadly scope out the cellular songs and melodies, as Siddhartha Mukherjee advises (2022), that we are learning serve to join living systems altogether.

Network biology, an interdisciplinary merger of computational and biological sciences, is vital understand cellular functioning and disease. A workshop on Future Directions in Network Biology was held at the University of Notre Dame in 2022, which brought together active researchers in this field. Typical topics were: inference and comparison of networks, multimodal data integration, heterogeneous, higher-order network analysis, machine learning, and network-based personalized medicine. Video recordings of the workshop presentations are publicly available on YouTube. This paper, co-authored mostly by the participants, summarizes the discussion and is expected to help shape short- and long-term visions for future computational and algorithmic research. (Excerpt)

Flam-Sherperd, Daniel, et al. Atom-by-atom protein generation and beyond with language models. arXiv:2308.09482.
We post an August entry by University of Toronto and Vector Institute reseachers including Alán Aspuru-Guzik to record much current activity in biocomputional studies which now join Large Language Models of AI neural machine learning methods. As the excerpt cites, a broad continuity across chemical, genetic, biochemical and onto linguistic phases bodes for an innately informational, universe to wumanverse, literacy to literacy procreative milieu. See also, for example, PEvoLM: Protein Sequence Evolutionary Information Language Model by Issar Arab at 2308.08578.

Protein language models learn powerful representations directly from sequences of amino acids. In contrast, chemical language models learn atom-level results of smaller molecules that include every atom, bond, and ring. In this work, we show that chemical language models can learn atom-level proteins which can generate the standard genetic code and far beyond it. The results demonstrate the potential for biomolecular design at the atom level using language models. (Exerpt)

Soares, Eduardo, et al. Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction. arXiv:2306.14919. Seven IBM researchers posted in Rio de Janeiro, Brazil and San Jose, USA including Dmitry Zubarev first describe current approaches as this broad field of biomolecule parsings actively shifts to deep machine learning methods. See also Artificial Intelligence-aided Protein Engineering from Topological Data Analysis to Deep Protein Language Models at 2307.14587 for another instance. A number of technique proposals are then advanced going forward. Altogether such novel literacies add more evidence for an affine genetic and protein equivalence.

Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development. (Excerpt)

Hu, Hu, Mengzhou, et al.. Evaluation of large language models for discovery of gene set function. arXiv:2309.04019. Seven UC San Diego computational geneticists including Trey Ideker carry out an initial, guided evaluation of this new machine intelligence method amd find it, at first usage, to be a reliable tool.

Gene set analysis is a mainstay of functional genomics, but it relies on partially curated databases. Here we evaluate the ability of OpenAI's GPT-4, a Large Language Model (LLM), to develop hypotheses about common gene functions from its embedded biomedical knowledge. We created a GPT-4 pipeline to label gene sets with names that summarize their consensus functions, substantiated by analysis text and citations. The ability to rapidly synthesize common gene functions positions LLMs as valuable genomics assistants. (Excerpt)

Conclusions When applied to study gene function, we have suspected that LLMs might produce statements, hypotheses, and references that would be error-prone and unusable. In fact, in our evaluations, GPT-4 typically did not falter, often with exemplary performance. We thus conclude that, given appropriate framing, the current general platform provides researchers with a new and powerful tool for gene set interpretation. (7)

Life's Corporeal Evolution Encodes and Organizes Itself: An EarthWinian Genesis Synthesis

Asche, Silke, et al. What it takes to solve the Origin(s) of Life: An integrated review of techniques. arXiv:2308.11665. We post this frontier 109 page entry by a worldwise array of OoLEN (Origin of Life Early-career Network) researchers to have it already on line. A longer review will ensue in turn.

Understanding the origin(s) of life (OoL) is a fundamental challenge for science in the 21st century. Research on OoL spans many disciplines, including chemistry, physics, biology, planetary sciences, computer science, mathematics and philosophy. The sheer number of different scientific perspectives relevant to the problem has resulted in the coexistence of diverse tools, techniques, data, and software in OoL studies. This has made communication between the disciplines relevant to the OoL extremely difficult because the interpretation of data, analyses, or standards of evidence can vary dramatically. Here, we hope to bridge this wide field of study by providing common ground via the consolidation of tools and techniques rather than positing a unifying view on how life emerges. We review the common tools and techniques that have been used significantly in OoL studies in recent years. In particular, we aim to identify which information is most relevant for comparing and integrating the results of experimental analyses into mathematical and computational models. This review aims to provide a baseline expectation and understanding of technical aspects of origins research, rather than being a primer on any particular topic. As such, it spans broadly -- from analytical chemistry to mathematical models -- and highlights areas of future work that will benefit from a multidisciplinary approach to tackling the mystery of life's origin. Ultimately, we hope to empower a new generation of OoL scientists by reviewing how they can investigate life's origin, rather than dictating how to think about the problem. (Abstract)

Silke, Asche, et al. What it takes to solve the Origin(s) of Life: An integrated review of techniques.. arXiv:2308.11665. As a good example of a 2020s spiral turn to a global research endeavor, over forty authors who make up an “Origin of Life Early-career Network” from across Europe, the USA, Japan and China, such as Martina Preiner, Stuart Harrison and Joanna Xavier scope a most comprehensive agenda from quantum chemistry and genetic replicators onto an evolutionary course. Topical aspects include Network Autocatalysis, Metagenomics, Information Theory, Synthetic Biology, Protocells for some 109 pages and backed by 680 references. A billion years or so later, just now a superorganic knowsphere can begin to achieve its retrospective observance.

Understanding the origin(s) of life (OoL) is a fundamental challenge for science in the 21st century. Research on OoL spans many disciplines from chemistry and physics to biology, planetary sciences, computer science, mathematics and philosophy. These diverse aspects has so far involved a contrast of techniques, data, and software in the field. Here, we hope to scope out and provide a common consolidation toward a unifying view on how life emerges and ultimately to empower a new generation of OoL scientists. (Excerpt)

Modeling Chemical Systems Both equilibrium and nonequilibrium approaches to modelling chemical systems are common in OoL studies. The nonequilibrium approach is based on chemical kinetic theory as well as the growing field of nonequilibrium thermodynamics, and it is used in environments that are driven out of equilibrium such as terrestrial atmospheres, protoplanetary disks, and biological enzyme catalysis. Finally, many biochemical and biological systems are modelled using the principles of chemical reaction networks - which share a framework with classical chemical kinetic theory. (37-38)

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