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Anaerobic oxidation of methane is a globally important, microbially mediated process reducing the emission of methane, a potent greenhouse gas.
The thiol redox proteome refers to all proteins whose cysteine thiols are subjected to various redox-dependent posttranslational modifications (PTMs) including S-glutathionylation (SSG), S-nitrosylation (SNO), S-sulfenylation (SOH), and S-sulfhydration (SSH). These modifications can impact various aspects of protein function, including activity, binding, conformation, localization, and interactions with other molecules. It is highly desirable to have robust redox proteomics methods that can provide global, site-specific, and stoichiometric quantification of redox PTMs in order to elucidate the complex redox landscape and identify novel redox regulators in signaling and regulation. Mass spectrometry-based redox proteomics has emerged as the primary platform for enabling the broad characterization of thiol PTMs in cells and tissues. Herein we review recent advances in MS-based redox proteomics approaches for quantitative profiling of redox PTMs in cells and tissues at physiological or oxidative stress conditions and highlight some recent applications using these enabling techniques. Considering the relative maturity of available methods, a primary emphasis will be on two types of modifications: 1) total oxidation (i.e., all reversible thiol modifications), the level of which represents the overall redox state, and 2) S-glutathionylation, a major form of reversible thiol oxidation. We also discuss the significance of stoichiometric measurements of thiol PTMs as well as future perspectives towards a better understanding of cellular redox regulatory networks in cells and tissues.
Abstract not provided.
Abstract Background Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model. Results We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.
The unfolded protein response (UPR) is a highly coordinated signaling network that alleviates endoplasmic reticulum (ER) stress, a condition induced by diverse environmental challenges in plants. Over the past two decades, substantial progress has been made in elucidating the genetic and molecular mechanisms of ER stress sensing and signal transduction in plants, largely through studies in the model plant Arabidopsis thaliana . These advances have established the UPR as a central regulator of proteostasis and underscored its broader relevance to plant growth and development and crop productivity under stress conditions. Despite this progress, critical knowledge gaps remain, particularly concerning the downstream biological processes required for growth recovery once ER stress has subsided and how these processes are coordinated by UPR regulators. Recent systems-level and integrative studies have begun to reveal critical roles of UPR signaling in pathways governing growth re-establishment and homeostasis of nutrient allocation and energy metabolism. In this review, we highlight recent findings on the functional roles of the plant UPR in recovery from ER stress, with a focus on mechanisms mediated by UPR regulators and downstream biological pathways that enable the transition from stress mitigation to growth restoration. Although this research area is still emerging, accumulating evidence supports a model in which the UPR functions as a dynamic regulatory network that actively coordinates post-stress physiological recovery to support plant fitness.
Abstract Background Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. Results We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Conclusions Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive.
In the last two years, two BioSentinel payloads were launched to space. The ISS mission launched in December 2021, and returned to the ground in August 2022 after successfully completing eight biological experiments while validating the different instruments. On the other hand, the deep space mission launched onboard Artemis I in November 2022, and is currently in a heliocentric orbit over 20 million kilometers away from the Earth. Even though all hardware subsystems were validated in deep space, the microfluidic subsystems experienced anomalies throughout the initial 6-month mission. The main goals of this presentation are (1) to present flight data from the deep space payload, including biology, fluidics, electronics, data processing, and mission operations, and (2) to discuss the lessons learned – what worked and what did not – from this unique complex mission, and how these lessons are aiding in the development of the Lunar Exploration Instrument for space biology Applications (LEIA) mission, launching to the lunar surface on a commercial lander in 2026. As of the writing of this abstract, the satellite continues to work nominally, communicating to Earth via the Deep Space Network (DSN) twice per week. Importantly, the mission received and extension to continue recording data on the deep space radiation environment on its way to solar maximum (i.e., higher probability of solar particle events). BioSentinel is supported by NASA Exploration Systems Development Mission Directorate (ESDMD).
This DOE-BES award provided conference support for the 32nd Western Photosynthesis Conference (WPC2023), held January 5–8, 2023 at the Bodega Bay Marine Laboratory in Bodega Bay, California. The Western Photosynthesis Conference is one of three regional photosynthesis meetings held annually in the United States and serves as a primary venue for early-career scientists—graduate students, postdoctoral researchers, and undergraduates—to present research and build professional networks alongside established investigators in the field. Photosynthesis research is central to the DOE Office of Science's Basic Energy Sciences mission, underpinning fundamental understanding of solar energy conversion, biofuels, and the biological transformation of light into chemical energy. DOE-BES funds were used, as proposed, to support registration and housing costs for 20 early-career participants (9 postdoctoral researchers, 9 graduate students, and 2 undergraduates) and to help defray travel costs for two junior invited speakers based outside California, for a total DOE-BES expenditure of $10,060. The meeting was executed as planned, with no significant deviations from the approved program, budget, or schedule. WPC2023 drew 76 registrants across six scientific topic areas, featured 8 of 9 invited/keynote speakers, and provided extensive opportunities for early-career scientists to present talks and posters, compete for recognition awards, and network with senior researchers in the field.
In plants, robust defense depends on the efficient and resilient trafficking supply chains to the site of pathogen attack. Though the importance of intracellular trafficking in plant immunity has been well established, a lack of clarity remains regarding the contribution of the various trafficking pathways in transporting immune-related proteins. We have recently identified a trans-Golgi network protein, TGN-ASSOCIATED PROTEIN 1 (TGNap1), which functionally links post-Golgi vesicles with the cytoskeleton to transport immunity-related proteins in the model plant species Arabidopsis thaliana. We propose new hypotheses on the various functional implications of TGNap1 and then elaborate on the surprising heterogeneity of TGN vesicles during immunity revealed by the discovery of TGNap1 and other TGN-associated proteins in recent years.
Micronutrients have demonstrated promise in managing inattention and emotional dysregulation in children with attention-deficit/hyperactivity disorder (ADHD). The biological mechanism by which micronutrients improve these symptoms remains unclear. One plausible pathway is through the gut-brain axis, the bi-directional communication network that links the gastrointestinal tract with the brain. This study examines changes in gut microbiome composition and diversity after micronutrients supplementation in children with ADHD (N=44) and sheds light on potential mechanisms responsible for the response to micronutrients as measured by clinician-rated global impression. Participants from this investigation represent a sub-group of the Micronutrients for ADHD in Youth (MADDY) study, a double blind randomized controlled study in which participants received either micronutrients or a placebo for 8 weeks, followed by an 8-week open label extension with micronutrients for all participants. Stool samples collected at baseline, week 8, and week 16 were analyzed using 16S rRNA amplicon sequencing targeting the V4 hypervariable region. Pairwise compositional analyses served as the primary means for investigating changes in gut microbiome composition between micronutrients versus placebo groups and responders versus non-responders. A significant change in microbial evenness, as measured by alpha diversity, was observed following micronutrients, and the phylum Actinobacteriota decreased in the micronutrients group compared to placebo. Additionally, two bacterial families: Rikenellaceae and Oscillospiraceae, exhibited a significant increase in change of gut microbiome composition following micronutrients between responders and non-responders. These findings suggest that micronutrients modulated the composition of the gut microbiome and point towards specific bacterial changes associated with response to micronutrients.
RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.
The ASR-supported Surface Atmosphere Integrated field Laboratory (SAIL) in the East River Watershed (ERW) of the Upper Colorado River Basin in southwestern Colorado ran from fall 2021 to spring 2023. Two monitoring sites were deployed in the East River Watershed as part of SAIL. The two sites were the Aerosol Observation System (AOS) located on Crested Butte Ski Mountain, and the ARM Mobile Facility (AMF-2), located at the Rocky Mountain Biological Laboratory in Gothic, Colorado. To gain a more comprehensive understanding of aerosols in complex, mountainous terrain, Handix Scientific deployed SAIL-Net, a distributed network of six measurement nodes spanning the domain of the SAIL research area from October 2021 to July 2023. Each node measured aerosol particles between 140 nm and 3.4 μm in diameter using a small particle counter (POPS, (Gao et al., 2016)), CNN using a miniature CCN counter (CloudPuck), and INP using the Time-Resolved Aerosol Filter Sampler (TRAPS, Creamean et al. (2018)). Our approach was similar to other studies that aimed to better characterize and understand aerosols and gas-phase pollutants using networks of lower-cost sensors (Caubel et al., 2019; Kelly et al., 2021; Asher et al., 2022). Such studies have identified neighborhood-level variations in pollutant concentrations (Schneider et al., 2017; Popoola et al., 2018; Caubel et al., 2019). Small-scale variations such as this are poorly represented in models and poorly measured by a single monitoring system (Caubel et al., 2019). Previous work has shown the representation error (the ability of measurements to represent a larger area) increases with complex orography, leading to decreases in model accuracy (Schutgens et al., 2017). The overall goal of SAIL-Net was to improve our understanding of the variability of aerosol in ERW, thus increasing our knowledge of aerosol-cloud interactions in this region and informing the usefulness of distributed networks of measurements for future studies.
The U.S. Department of Energy Atmospheric System Research (ASR)-supported Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in the East River Watershed (ERW) of the Upper Colorado River Basin in southwestern Colorado ran from fall 2021 to spring 2023. Two monitoring sites were deployed in the ERW as part of SAIL. The two sites were the Aerosol Observation System (AOS) located on Crested Butte Ski Mountain, and the second ARM Mobile Facility (AMF2), located at the Rocky Mountain Biological Laboratory in Gothic, Colorado. To gain a more comprehensive understanding of aerosols in complex, mountainous terrain, Handix Scientific deployed SAIL-Net, a distributed network of six measurement nodes spanning the domain of the SAIL research area from October 2021 to July 2023. Each node measured aerosol particles between 140 nm and 3.4 μm in diameter using a small portable optical particle spectrometer (POPS; Gao et al. 2016), cloud condensation nuclei (CNN) using a miniature CCN counter (CloudPuck), and ice nucleating particles (INP) using the time-resolved aerosol filter sampler (TRAPS; Creamean et al. 2018). Our approach was similar to other studies that aimed to better characterize and understand aerosols and gas-phase pollutants using networks of lower-cost sensors (Caubel et al. 2019, Kelly et al. 2021, Asher et al. 2022). Such studies have identified neighborhood-level variations in pollutant concentrations (Schneider et al. 2017, Popoola et al. 2018, Caubel et al. 2019). Small-scale variations such as this are poorly represented in models and poorly measured by a single monitoring system (Caubel et al. 2019). Previous work has shown the representation error (the ability of measurements to represent a larger area) increases with complex orography, leading to decreases in model accuracy (Schutgens et al. 2017). The overall goal of SAIL-Net was to improve our understanding of the variability of aerosol in the ERW, thus increasing our knowledge of aerosol-cloud interactions in this region and informing the usefulness of distributed networks of measurements for future studies. We met this goal by answering the following science questions: 1. What is the aerosol temporal variability, and how does aerosol inhomogeneity vary seasonally? Is there significant seasonal variability in sources, or are short-term meteorological conditions the most important determining factor in sources for cloud nuclei? 2. What is the aerosol spatial variability? What are the aerosol characteristics at cloud base, presumably the particles most representative of those acting as cloud nuclei? 3. How should measurement networks be designed to capture aerosol-cloud interactions, and what do they need to measure? Can a single measurement site accurately represent aerosol properties in regions of complex terrain? SAIL-Net consisted of six measurement nodes spread across the ERW near Crested Butte, Colorado. The primary objective in site placement was to select locations that captured the vertical variation in aerosol properties while also spanning the domain of the SAIL campaign. The elevation of the sites ranged from roughly 2750 m along the valley floor of the ERW to approximately 3500 m near the top of Crested Butte Mountain, which is one of the taller peaks in the ERW. The farthest distance between sites was 14 km, while the closest two sites were approximately 1 km apart. Two of the sites were collocated with the ARM SAIL sites; our instruments sat on top of one of the trailers at AOS and another one of our sites was located in a meadow just above AMF2.
X-ray fluorescence (XRF) microscopy is a powerful technique for quantifying the distribution of elements in complex materials, which makes it a crucial imaging technique across a wide range of disciplines in physical and biological sciences, including chemistry, materials science, microbiology, and geosciences. However, as a scanning microscopy technique, the spatial resolution of XRF imaging is inherently constrained by the x-ray probe profile and scanning step size. Here we propose a dual-branch machine learning (ML) model, which can extract scale-variant features and bypass abundant low-frequency information separately, to enhance the spatial resolution of the XRF images by mitigating the effects of blurring from the probe profile. The model is trained by simulated natural images, and a two-stage training strategy is used to overcome the domain gap between the natural images and experimental data. The tomography reconstruction from enhanced XRF projections shows an improvement in resolution by a scale factor of four and reveals distinct internal features invisible in low-resolution XRF within a battery sample. This study offers a promising approach for obtaining high-resolution XRF imaging from its low-resolution version, paving the way for future investigations in a broader range of disciplines and materials.
Camelina has great potential to become a sustainable high energy-yielding source of biofuel in the US. This project aims to address two critical needs for realizing this potential: to increase seed size and oil content for improved seedling establishment and oil yield, and to optimize oil quality for satisfactory fuel properties. Specifically, quantitative trait loci (QTL) and molecular markers associated with these important traits will be identified using high-density genome maps and repeated field trials in Montana and Washington states. Modern genomics and biotechnological approaches will be employed to uncover novel molecular mechanisms (including genes and gene networks regulated by microRNAs and transcription factors) regulating fatty acid modification, oil accumulation and seed size in Camelina.
While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called “thermal neuristors”, which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. In conclusion, this study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.
Ocean surface radiation measurement best practices have been developed as a first step to support the interoperability of radiation measurements across multiple ocean platforms and between land and ocean networks. This document describes the consensus by a working group of radiation measurement experts from land, ocean, and aircraft communities. The scope was limited to broadband shortwave (solar) and longwave (terrestrial infrared) surface irradiance measurements for quantification of the surface radiation budget. Best practices for spectral measurements for biological purposes like photosynthetically active radiation and ocean color are only mentioned briefly to motivate future interactions between the physical surface flux and biological radiation measurement communities. Topics discussed in these best practices include instrument selection, handling of sensors and installation, data quality monitoring, data processing, and calibration. It is recognized that platform and resource limitations may prohibit incorporating all best practices into all measurements and that spatial coverage is also an important motivator for expanding current networks. Thus, one of the key recommendations is to perform interoperability experiments that can help quantify the uncertainty of different practices and lay the groundwork for a multi-tiered global network with a mix of high-accuracy reference stations and lower-cost platforms and practices that can fill in spatial gaps.
Functional network analysis of 112 GWAS-identified candidate genes for yield-related traits in pennycress ( Thlaspi arvense ). Uses RWRtools Lines of Evidence to propagate Arabidopsis orthologs through a nine-layer multiplex functional network.