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Keiichi Inoue

Publications and source records attributed to Keiichi Inoue.

3 recordsLinked to original sources

White Paper on Phototrophic Biosignatures: Research Priorities for the Search for Life on Other Worlds

Photosynthesis is of prime interest in the telescopic search for life beyond the Solar System, because, on Earth, oxygenic photosynthesis produces two strong "biosignatures," global scale signs of life that can be seen from space: atmospheric oxygen and the Vegetation Red Edge (VRE). The VRE is the spectral reflectance signature of plant leaves, characterized by a step-like increase in reflectance from the red to the near-infrared. The absorption in the red is due to chlorophyll $\textit{a}$ (Chl $\textit{a}$). While Chl $\textit{a}$ dominates our planet, the Earth harbors diverse phototrophic organisms in niche environments possessing other pigments that produce edge-like spectral features across the UV-VIS-NIR, naturally suggesting diverse signatures that could be found on other planets where phototrophic life is adapted to other stars. However, the astrobiology community is very much at an early stage in its ability to constrain the probability that an observation of another planet has detected a sign of photosynthetic life. This white paper identifies critical research questions to advance to a predictive capability the search for phototrophic biosignatures. These questions pertain to the origins, key features, diversity, and potential for alternative adaptations in fundamental aspects of light harvesting; the electron transfer pathway in photosynthesis; rhodopsin-based proton-pumping; and carbon fixation. We discuss the need to constrain how evolution and ecology affect the scaling up of these molecular mechanisms to be potentially detectable by a direct imaging mission. The research questions and recommendations presented here are cross-linked to those posed by the NASA Astrobiology Strategy 2015, and to the Focus Areas of the upcoming NASA Decadal Astrobiology Exploration Strategy (DARES).

astro-ph.IM

Spin polarization driven by molecular vibrations leads to enantioselectivity in chiral molecules

Chirality pervades multiple scientific domains-physics, chemistry, biology, and astronomy-and profoundly influences their foundational principles. Recently, the chirality-induced spin selectivity (CISS) phenomenon has captured significant attention in physical chemistry due to its potential applications and intriguing underlying physics. Despite its prominence, the microscopic mechanisms of CISS remain hotly debated, hindering practical applications and further theoretical advancements. Here we challenge the established view that attributes CISS-related phenomena to current-induced spin polarization and electron transport across interfaces. We propose that molecular vibrations in chiral molecules primarily drive spin polarization, thereby governing CISS. Employing an electrochemical cell paired with a precisely engineered magnetic multilayer, we demonstrate that the magnetic interactions akin to interlayer exchange coupling are crucial for CISS. Our theoretical study suggests that molecular vibrations facilitate chirality-dependent spin polarization, which plays a pivotal role in CISS-related phenomena such as magnetoresistance and enantiomer separation using ferromagnets. These findings necessitate a paradigm shift in the design and analysis of systems in various scientific fields, extending the role of spin dynamics from traditional areas such as solid-state physics to chemical reactions, molecular biology, and even drug discovery.

cond-mat.mtrl-sci

Active learning for level set estimation under input uncertainty and its extensions

Testing under what conditions the product satisfies the desired properties is a fundamental problem in manufacturing industry. If the condition and the property are respectively regarded as the input and the output of a black-box function, this task can be interpreted as the problem called Level Set Estimation (LSE) -- the problem of identifying input regions such that the function value is above (or below) a threshold. Although various methods for LSE problems have been developed so far, there are still many issues to be solved for their practical usage. As one of such issues, we consider the case where the input conditions cannot be controlled precisely, i.e., LSE problems under input uncertainty. We introduce a basic framework for handling input uncertainty in LSE problem, and then propose efficient methods with proper theoretical guarantees. The proposed methods and theories can be generally applied to a variety of challenges related to LSE under input uncertainty such as cost-dependent input uncertainties and unknown input uncertainties. We apply the proposed methods to artificial and real data to demonstrate the applicability and effectiveness.

stat.ML