One-carbon (bio ?) Geochemistry in Subsurface Waters of the Serpentinizing Coast Range Ophiolite

Serpentinization - the aqueous alteration of ultramafic rocks - typically imparts a highly reducing and alkaline character to the reacting fluids. In turn, these can influence the speciation and potential for metabolism of one-carbon compounds in the system. We examined the aqueous geochemistry and assessed the biological potential of one-carbon compounds in the subsurface of the McLaughlin Natural Reserve (Coast Range Ophiolite, California, USA). Fluids from wells sunk at depths of 25-90 meters have pH values ranging from 9.7 to 11.5 and dissolved inorganic carbon (DIC concentrations) generally below 60 micromolar. Methane is present at concentrations up to 1.3 millimolar (approximately one-atmosphere saturation), and hydrogen concentrations are below 15 nanomolar, suggesting active consumption of H2 and production of CH4. However, methane production from CO2 is thermodynamically unfavorable under these conditions. Additionally, the speciation of DIC predominantly into carbonate at these high pH values creates a problem of carbon availability for any organisms that require CO2 (or bicarbonate) for catabolism or anabolism. A potential alternative is carbon monoxide, which is present in these waters at concentrations 2000-fold higher than equilibrium with atmospheric CO. CO is utilized in a variety of metabolisms, including methanogenesis, and bioavailability is not adversely affected by pH-dependent speciation (as for DIC). Methanogenesis from CO under in situ conditions is thermodynamically favorable and would satisfy biological energy requirements with respect to both Gibbs Energy yield and power

Carbon assimilation strategies in ultrabasic groundwater: clues from the integrated study of a serpentinization-influenced aquifer

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Seyler, L. M., Brazelton, W. J., McLean, C., Putman, L. I., Hyer, A., Kubo, M. D. Y., Hoehler, T., Cardace, D., & Schrenk, M. O. . Carbon assimilation strategies in ultrabasic groundwater: clues from the integrated study of a serpentinization-influenced aquifer. mSystems, 5(2), (2020): e00607-00619, doi: 10.1128/mSystems.00607-19.Serpentinization is a low-temperature metamorphic process by which ultramafic rock chemically reacts with water. Such reactions provide energy and materials that may be harnessed by chemosynthetic microbial communities at hydrothermal springs and in the subsurface. However, the biogeochemistry mediated by microbial populations that inhabit these environments is understudied and complicated by overlapping biotic and abiotic processes. We applied metagenomics, metatranscriptomics, and untargeted metabolomics techniques to environmental samples taken from the Coast Range Ophiolite Microbial Observatory (CROMO), a subsurface observatory consisting of 12 wells drilled into the ultramafic and serpentinite mélange of the Coast Range Ophiolite in California. Using a combination of DNA and RNA sequence data and mass spectrometry data, we found evidence for several carbon fixation and assimilation strategies, including the Calvin-Benson-Bassham cycle, the reverse tricarboxylic acid cycle, the reductive acetyl coenzyme A (acetyl-CoA) pathway, and methylotrophy, in the microbial communities inhabiting the serpentinite-hosted aquifer. Our data also suggest that the microbial inhabitants of CROMO use products of the serpentinization process, including methane and formate, as carbon sources in a hyperalkaline environment where dissolved inorganic carbon is unavailable.We thank McLaughlin Reserve, in particular Paul Aigner and Cathy Koehler, for hosting sampling at CROMO and providing access to the wells, A. Daniel Jones and Anthony Schilmiller for their advice regarding metabolite extraction and mass spectrometry, Elizabeth Kujawinski for her guidance in metabolomics data analysis and interpretation, and Julia McGonigle, Christopher Thornton, and Katrina Twing for assistance with metagenomic and computational analyses

Learning Features Across Tasks and Domains

The absence of in-domain labeled data hinders the applicability of powerful deep neural networks. Unsupervised Domain Adaptation (UDA) methods have emerged to exploit such models even when labeled data is not available in the target domain. All these techniques aim to reduce the distribution shift problem that afflicts these models when trained on one dataset and tested in a different one. However, most of the works, do not consider relationships among tasks to further boost performances. In this thesis, we study a recent method called AT/DT (Across Tasks Domain Transfer), that seeks to apply Domain Adaptation together with Task Adaptation, leveraging on the correlation of two popular Vision tasks such as Semantic Segmentation and Monocular Depth Estimation. Inspired by the Domain Adaptation literature, we propose many extensions to the original work and show how these enhance the framework performances. Our contributions are applied at different levels: we first study how different architectures affect the transferability of features across tasks. We further improve performances by deploying Adversarial training. Finally, we explore the possibility of replacing Depth Estimation with popular Self-supervised tasks, demonstrating that two tasks must be semantically connected to be able to transfer features among them

Risk Perception of a Major Earthquake Event at the Cascadia Subduction Plate Boundary

The Cascadia Subduction Zone (CSZ) is a tectonic plate boundary that is located about 64 to 128 kilometers off the west coast from Northern California to Vancouver Island, British Columbia (Cascadia Region Earthquake Workgroup, 2013). Subduction faults are on a cycle of a pressure build up then quick release, materializing as a megathrust earthquake. These faults have the potential to produce earthquakes of the highest magnitude. Paleoseismic studies and Native American oral history have proved that the last CSZ earthquake occurred in 1700 and likely had a magnitude of 9.0 (Finkbeiner, 2015, Nelson et al., 2006). Most probability analyses suggest that there is a 7 to 15% chance of a CSZ earthquake in the next 50 years (Lewis et al., 2007, Buylova et al., 2019). Is the Pacific Northwest prepared for a high magnitude earthquake and tsunami event? The purpose of the study is to document if and how individuals, communities, and town and state governments are preparing for the CSZ megathrust earthquake. A local seismology expert, two members of different emergency preparation and response groups, and a town official were interviewed. The interviews were recorded, transcribed, and compared. The six most commonly mentioned themes between the participants were identified and discussed. These themes include: (1) public awareness; (2) what motivates people to prepare; (3) community building; (4) cannot rely on government support; and (5) failing infrastructure and utilities. All participants believed that there is a high level of awareness of the geohazards involving the CSZ, however, awareness does not equate to taking preparation measures. Participants revealed different levels of risk perceptions and beliefs on how much individual preparation is necessary. Interpreting the interview data in our current societal context suggests that environmental justice and vulnerable populations’ needs are important issues in the context of a CSZ earthquake and tsunami event

High pH microbial ecosystems in a newly discovered, ephemeral, serpentinizing fluid seep at YanartaÅŸ (Chimera), Turkey

Gas seeps emanating from ophiolites at Yanartaş (Chimaera), Turkey, have been documented for thousands of years. Active serpentinization produces hydrogen and a range of carbon gases that may provide fuel for life. Here we report a newly discovered, ephemeral fluid seep emanating from a small gas vent at Yanartaş. Fluids and biofilms were sampled at the source and points downstream. We describe site conditions, and provide microbiological data in the form of enrichment cultures, scanning electron microscopy (SEM), carbon and nitrogen isotopic composition of solids, and PCR screens of nitrogen cycle genes. Source fluids are pH 11.95, with a Ca:Mg of ~200, and sediments under the ignited gas seep measure 60°C. Collectively, these data suggest the fluid is the product of active serpentinization at depth. Source sediments are primarily calcite and alteration products (chlorite and montmorillonite). Downstream, biofilms are mixed with montmorillonite. SEM shows biofilms distributed homogeneously with carbonates. Organic carbon accounts for 60% of the total carbon at the source, decreasing downstream to <15% as inorganic carbon precipitates. δ13C ratios of the organic carbon fraction of solids are depleted (−25 to −28 ‰) relative to the carbonates (−11 to −20‰). We conclude that heterotrophic processes are dominant throughout the surface ecosystem, and carbon fixation may be key down channel. δ15N ratios ~ 3‰, and absence of nifH in extracted DNA suggest that nitrogen fixation is not occurring in sediments. However, the presence of narG and nirS at most locations and in enrichments indicates genomic potential for nitrate and nitrite reduction. This small seep with shallow run-off is likely ephemeral, but abundant preserved microterracettes in the outflow and the surrounding area suggest it has been present for some time. This site and others like it present an opportunity for investigations of preserved deep biosphere signatures, and subsurface-surface interactions

Implementazione del modello di Biham-Middleton-Levine sulla Parallella Board

In questa tesi si descrive l’architettura hardware e software della Parallella Board, un supercomputer delle dimensioni di una carta di credito che include una cpu ARM A9 dual core e un coprocessore Epiphany con 16 o 64 core. Grazie al suo basso costo e ad un hardware particolare, si tratta di un ottimo strumento sia per il programmatore esperto sia per lo studente che vuole avvicinarsi alla programmazione parallela. Tuttavia, a causa della limitata documentazione e dei pochi e complessi esempi disponibili, programmare la Parallella potrebbe richiedere un notevole impegno; l’obbiettivo è dunque quello di fornire una panoramica dettagliata della Parallella per aiutare chiunque sia interessato nello sviluppo di algoritmi paralleli a familiarizzare velocemente con questo dispositivo. Per comprendere nel dettaglio come programmare la Parallella Board, vengono forniti una serie di esempi introduttivi prima di arrivare ad un esempio finale più complesso che consiste in un’implementazione del modello di Biham-Middleton-Levine; nonostante sia possibile utilizzare diverse estensioni per il calcolo parallelo come OpenMP e MPI, si è scelto di scrivere i programmi utilizzando il C e le librerie di supporto fornite nativamente dall’eSDK (Epiphany Software development Kit)

Learning with limited data

In recent years, Deep Learning techniques have demonstrated remarkable achievements across various Computer Vision tasks, frequently surpassing human capabilities. Nevertheless, these data-driven methodologies often demand large volumes of annotated data, necessitating laborious and costly manual annotation procedures. The objective of this thesis is to introduce novel methods designed to mitigate this challenge by harnessing knowledge obtained from diverse domains or tasks, even in the presence of limited annotations. This challenge is commonly known as the Transfer Learning problem. Our exploration will delve into the forefront of Transfer Learning, with a predominant emphasis on the advancement of techniques for Domain Adaptation in diverse computer vision tasks. This research journey begins with a comprehensive investigation into 2D Semantic Segmentation, and we demonstrate how other tasks such as Depth Estimation and Edge Detection can enhance the adaptability of models across different visual domains. Subsequently, the exploration extends to the realm of 3D point cloud classification, where the challenges posed by diverse domain shifts are addressed once again exploiting auxiliary tasks such as shape reconstruction or recent Self-Supervised techniques. The proposed works for 2D Semantic Segmentation and 3D point cloud classification lay the foundation for the development of novel frameworks aimed at tackling the challenging task of multi-modal Domain Adaptation for 3D Semantic Segmentation, where multiple sensors such as RGB cameras and LiDARs are available. Finally, we shed some light on a new exciting and emerging topic which is solving common vision tasks on Neural Fields, which are an emerging paradigm used to represent signals such as images or 3D shapes. We will specifically focus on the 3D scenario, and in the context of Transfer Learning, show for the first time how acting directly on Neural Fields allows the possibility to transfer knowledge among different representations such as from 3D point clouds to meshes

Nonequilibrium clumped isotope signals in microbial methane

Methane is a key component in the global carbon cycle with a wide range of anthropogenic and natural sources. Although isotopic compositions of methane have traditionally aided source identification, the abundance of its multiply-substituted “clumped” isotopologues, e.g., 13CH3D, has recently emerged as a proxy for determining methane-formation temperatures; however, the impact of biological processes on methane’s clumped isotopologue signature is poorly constrained. We show that methanogenesis proceeding at relatively high rates in cattle, surface environments, and laboratory cultures exerts kinetic control on 13CH3D abundances and results in anomalously elevated formation temperature estimates. We demonstrate quantitatively that H2 availability accounts for this effect. Clumped methane thermometry can therefore provide constraints on the generation of methane in diverse settings, including continental serpentinization sites and ancient, deep groundwaters.National Science Foundation (U.S.) (EAR-1250394)National Science Foundation (U.S.) (EAR-1322805)Deep Carbon Observatory (Program)Natural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft (Gottfried Wilhelm Leibniz Program)United States. Dept. of Defense (National Defense Science and Engineering Graduate Fellowship)Neil & Anna Rasmussen FoundationGrayce B. Kerr Fund, Inc. (Fellowship)MIT Energy Initiative (Shell-MITEI Graduate Fellowship)Shell International Exploration and Production B.V. (N. Braunsdorf and D. Smit of Shell PTI/EG grant

RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation

Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations. Reliance on multi-task learning to align features across domains has been the standard way to tackle it. In this paper, we take a different path and propose RefRec, the first approach to investigate pseudo-labels and self-training in UDA for point clouds. We present two main innovations to make self-training effective on 3D data: i) refinement of noisy pseudo-labels by matching shape descriptors that are learned by the unsupervised task of shape reconstruction on both domains; ii) a novel self-training protocol that learns domain-specific decision boundaries and reduces the negative impact of mislabelled target samples and in-domain intra-class variability. RefRec sets the new state of the art in both standard benchmarks used to test UDA for point cloud classification, showcasing the effectiveness of self-training for this important problem