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Diverse Cooccurring Metabolisms Support Sulfur and Methane Cycling in Wetland Surficial Sediments
No full textThis article was originally published in Journal of Geophysical Research Biogeosciences . The version of record is available at: https://doi.org/10.1029/ 2024JG008478
© 2025. The Author(s).This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use distribution and reproduction in any medium, provided the original work is properly cited.The Prairie Pothole Region (PPR) of North America contains millions of small depressional wetlands with some of the highest methane (CH4) fluxes ever reported in terrestrial ecosystems. In saturated soils, two conventional paradigms are (a) methanogenesis is the final step in the redox ladder, occurring only after more thermodynamically favorable electron acceptors (e.g., sulfate) are reduced, and (b) CH4 is primarily produced by acetoclastic and hydrogenotrophic pathways. However, previous work in PPR wetlands observed co‐occurrence of sulfate‐reduction and methanogenesis and the presence of diverse methanogenic substrates (i.e., methanol, DMS). This study investigated how methylotrophic methanogenesis—in addition to acetoclastic
and hydrogenotrophic methanogenesis—significantly contributes to CH4 flux in surface sediments and thus allows for the co‐occurrence of competing redox processes in PPR sediments. We addressed this aim through
field studies in two distinct high CH4 emitting wetlands in the PPR complex, which coupled microbial
community compositional and functional inferences with depth‐resolved electrochemistry measurements in surficial wetland sediments. This study revealed methylotrophic methanogens as the dominant group of methanogens in the presence of abundant organic sulfate esters, which are likely used for sulfate reduction. Resulting high sulfide concentrations likely caused sulfide toxicity in hydrogenotrophic and acetoclastic methanogens. Additionally, the use of non‐competitive substrates by many methylotrophic methanogens allows these metabolisms to bypass thermodynamic constraints and can explain co‐existence patterns of sulfate‐reduction and methanogenesis. This study demonstrates that the current models of methanogenesis in wetland ecosystems insufficiently represent carbon cycling in some of the highest CH4 emitting environments.Funding for this work was provided by the National Science Foundation (Projects2029645 (MP, BMT, WAA), 2029665(DX, YPC), 2029686 (EKB, MJW)) and by U.S. Department of Energy (DOE)Office of Science, Office of Biological and Environmental Research (BER) grant DE‐SC0023084 (EKB, MJW). A portion of the research described in this paper was performed using Beamline 06B1‐1(SXRMB) at the Canadian Light Source, a national research facility of the University of Saskatchewan, which is supported by the Canada Foundation for Innovation(CFI), the Natural Sciences and Engineering Research Council (NSERC),the National Research Council (NRC), the Canadian Institutes of Health Research(CIHR), the Government of Saskatchewan ,and the University of Saskatchewan.
Thanks to beamline scientist Mohsen Shakouri for assisting in synchrotron data collection. A portion of this research used Beamline 7‐BM (QAS) of the National Synchrotron Light Source II, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under Contract No.DE‐SC0012704. Thanks to beamline scientists Lu Ma and Stephen Ehrlich for their support. The authors thank Matthew Solensky and Sheel Bansal from the USGS, Anthony Sigman‐Lowery(University of Delaware) and Cole Stenberg (UMN) for assistance with field sampling and helpful discussion
Sustainability Nexus AID: soil health
No full textThis article was originally published in Sustainability Nexus Forum. The version of record is available at: https://doi.org/10.1007/s00550-025-00560-6.
© The Author(s) 2025.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.The Sustainability Nexus Analytics, Informatics, and Data (AID) Programme of the United Nations University (UNU), aims to provide information, data, computational, and analytical tools to support the sustainable management and long-term security of natural resources using a nexus approach. This paper introduces the Soil Health Module of the Sustainability Nexus AID Programme. Healthy soil is crucial for life on Earth, and it is essential for ecosystem services and functioning, access to clean water, socioeconomic structure, biodiversity, and food security for the growing population of the world. Healthy soils contribute to mitigating the effects of climate change and reduce the consequences of extreme events such as flooding and drought. Healthy soils influence the hydrologic cycle by regulating transpiration, water infiltration, and soil water evaporation affecting land–atmosphere interactions. The Soil Health Module of the UNU Sustainability Nexus AID Programme aims to evolve into the ultimate focal point, supporting a diverse array of stakeholders with state-of-the-art data and tools that are essential for soil health monitoring and projection. This paper discusses the importance of adopting a nexus approach for ensuring soil health, explores the AID tools currently at our disposal for quantifying and predicting soil health, and concludes with recommendations for future effort and direction within the Sustainability Nexus AID Programme concerning soil health.The input of NS, MA, PB, DAR, PS and GT contributes to the project AI4SoilHealth funded by European Union’s Horizon Europe research and innovation programme under grant agreement No. 101086179. NS would like to acknowledge funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project number 497539130. The UNU Sustainability Nexus AID Programme is thankful to an international group of leading scientists for their valuable contributions since its inception. The Programme also acknowledges the partial financial support of Germany’s Ministry of Education and Research (BMBF) and Global Affairs Canada (GAC).
Open Access funding enabled and organized by Projekt DEAL
Transmembrane protein residue contact prediction using novel machine learning approaches and pretrained protein language models
No full textLiao, LiTransmembrane (TM) proteins represent a significant portion of all known proteins and play a crucial role in many biological processes, such as facilitating the transport of molecules, ions, and information between a cell and its external environment. It’s estimated that they account for approximately 20-30% of all protein-coding genes in humans. Despite their abundance and importance, only a very small portion has been determined experimentally because of the difficulty of obtaining well-ordered crystals and the high cost of conducting in vitro experiments. Such a difficulty might hinder the understanding of their function. The need for developing computational tools to determine their structure, therefore, becomes essential. However, compared to globular proteins, computationally determining the structure of TM proteins can be harder due to the limited availability of high-resolution structures to be used as templates and training examples for structural modeling and prediction. Residue contact prediction is one of the most successful computational approaches to reduce the huge search space for the TM protein fold and generate a high-quality 3D structure. Determining the structure of the protein can reveal invaluable information about its function. In this work, we explore the current advances in the field, investigate the effectiveness of different learning approaches, propose and develop novel machine/deep learning techniques for generating a high-quality contact map for both inter-chain and intra-chain residues in alpha-helical TM proteins. In addition, we assess the accuracy of the different proposed models and show that the proposed work can enhance the contact map prediction and ultimately produce a better 3D structure. ☐ In the first chapter, we investigate the contact map prediction task of alpha-helical TM proteins from a different angle using a transductive learning approach. Identifying the interaction between the helices within the membrane greatly affects their tilt angle and relative position, thus impacting the overall protein structure. We utilize transductive learning by incorporating the unlabeled test data during training to address the scarcity of labeled data, which is common in the prediction of amino acid residue contacts, and to improve the model accuracy. Using features derived fromprotein structures, we compare the predictive performance of transductive support vector machine (SVM) and inductive SVM in identifying helix-helix residue contacts, with the aim of determining the specific conditions and limitations under which TSVMs excel. Then, we explore potential solutions to mitigate the performance degradation of the transductive model. We introduce an early stop technique TSVMES that produces a more accurate model, outperforming the state-of-the-art TSVM by 5% when tested on a set of benchmarks of transmembrane proteins. ☐ In the second chapter, we investigate the feasibility of incorporating structural features into the classifier. Most current TM protein residue contact predictors rely solely on features extracted from protein sequences to predict residue contacts. However, using these features alone leads to a low-accuracy contact map and, subsequently, to a poor 3D structure. Other models attempt to exploit features extracted from 3Dprotein structures to produce a better representative contact model. Nevertheless, this approach is not applicable in real-life scenarios where the structure is not available during the model testing phase, making it a chicken-and-egg dilemma. Therefore, we propose a novel approach that utilizes atomic features extracted from known TM protein 3D structures to enable the model to train on these features and transfer this knowledge to the test data, which lack atomic features, to improve the prediction of the contact of the TM protein residue. Our proposed method, AT-TSVM, employs Transductive Support Vector Machines with transfer and active learning to improve contact prediction accuracy. The results indicate that our proposed model can boost the accuracy of contact prediction by an average of 5 to 6% on the inductive classifier and 3 to 4% over the transductive classifier. ☐ In the third chapter, we utilize large protein language models to generate an accurate contact map for alpha-helical TM proteins. The majority of previous studies employ techniques that rely on statistical analysis of the sequence to infer connections between residues. A few recent techniques, which are based on natural language processing models, have been successful in achieving this goal. Nevertheless, the majority of these techniques and models are designed for globular proteins and are not tailored for specific protein types like Transmembrane Proteins. Therefore, we propose a Transmembrane Protein Helices Contacts predictor (TMHC-MSA) that utilizes features extracted by a protein language model (MSA Transformer) and incorporates neighborhood information using a feature window to enhance the quality of the produced contact map. Our proposed model demonstrates superior performance by successfully outperforming the state-of-the-art method by an average of 7% in terms of L precision and even surpassing the MSA Transformer by an average of 2.5% on the same metric. Furthermore, we demonstrate that the more accurate contact map produced by our model can be used to generate a more accurate 3D structure. ☐ In the fourth chapter, we dive deeper to explore the dimerization of bitopic TM proteins. Most bitopic transmembrane proteins associate with each other to stabilize their structure by forming dimers. This association leads to the activation of downstream specific cellular functions. Therefore, being able to accurately identify interface residues in a given dimer is important to understand its function, and has been a challenging pursuit of many computational methods. In this chapter, we break down the dimerization residue contact prediction into two tasks. In the first task, we propose a model that leverages structural features extracted from the field of molecular dynamics alongside other features from various domains to predict interface residues in α-helical TM dimers. The accurate prediction of interface residues has potential applications in pharmaceutical drug design. The results reveal key limitations in the ability of state-of-the-art multimer models, including AlphaFold2-Multimer and RoseTTAFold2, to precisely identify these interface residues. Therefore, we introduced TMH-ID, a novel machine learning model which integrates various sequence-based features, including large protein language model coupling scores and TM-specific motifs, in addition to structure-based features extracted from the predicted structure of PREDDIMER. In particular, our proposed model achieved the highest mean F1 score, outperforming several advanced baselines such as THOIPA, MSA Transformer, and ProteinBERT. Furthermore, TMH-ID outperforms other multimer structure predictors RoseTTAFold2, AlphaFold2Multimer, and PREDDIMER in interface residue prediction across the Crystal subset. ☐ In the fifth chapter, we explore the prediction of inter-chain residue contacts in TM homodimers and present ICM-MD, a novel machine learning framework for predicting inter-chain residue contacts in α-helical TM homodimers. In this chapter, we propose two models to address the scarcity of available structures necessary to develop an accurate classifier to identify these contacts. This is achieved by training the models on a large database of molecular dynamics simulated dimers (Membranome) and integrating transmembrane-specific sequence and structural features. Our models adopt a residue-pair-centric learning paradigm to address the limited availability of training data and to enhance generalizability to unseen examples. The first model employs a lightweight and interpretable feed-forward neural network architecture that is com-putationally efficient and scalable. The second model implements a more advanced architecture based on graph convolutional networks (GCNs), enabling the effective integration of information from neighboring residues to capture richer structural context. This is achieved through the message-passing mechanism, which facilitates the exchange of information between contacting residues, thereby enabling each residue to incorporate context from its interaction partners. To the best of our knowledge, this is the first study to leverage molecular dynamics–based structural models as a surrogate ground truth for training an interchain contact predictor in TM proteins. The results show that the proposed simple model consistently outperforms state-of-the-art models, including DeepHomo1, DeepHomo2, Glinter, and DeepTMP in multiple evaluation metrics. Moreover, the advanced GCN-based model surpasses all the other models, delivering consistently stable performance across all evaluation metrics.University of Delaware, Department of Computer and Information SciencesPh.D
Mental Illness and Substance Use Disorder Stigma: Mapping Pathways Between Structures and Individuals to Accelerate Research and Intervention
No full textThis article was originally published in Annual Review of Clinical Psychology. The version of record is available at: https://doi.org/10.1146/annurev-clinpsy-081423-023228.
Copyright © 2025 by the author(s).
This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.Researchers, interventionists, and clinicians are increasingly recognizing the importance of structural stigma in elevating the risk of mental illnesses (MIs) and substance use disorders (SUDs) and in undermining MI/SUD treatment and recovery. Yet, the pathways through which structural stigma influences MI/SUD-related outcomes remain unclear. In this review, we aim to address this gap by summarizing scholarship on structural MI/SUD stigma and identifying pathways whereby structural stigma affects MI/SUD-related outcomes. We introduce a conceptual framework that describes how structural-level stigma mechanisms influence the MI/SUD treatment cascade via (a) interpersonal- and individual-level stigma mechanisms and (b) mediating processes among people with MI/SUD (i.e., access to resources, psychological responses, behavioral responses, social isolation). We consider intersections between MI/SUD stigma and stigma based on race/ethnicity, gender identity, and sexual orientation. Finally, we discuss the implications of this review for future research, interventions, and clinical practice
Preface to theme issue about multi-messenger gravitational lensing
No full textThis article was originally published in Philosophical Transactions of the Royal Society A. The version of record is available at: https://doi.org/10.1098/rsta.2024.0133.
© 2025 The Author(s).
Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.Multi-messenger gravitational lensing combines multi-messenger astronomy with gravitational lensing. The first gravitational lensing observations occurred during the 1919 total solar eclipse, and were published in the Philosophical Transactions of the Royal Society A, thus providing early support for Einstein’s theory of General Relativity [1]. Systematic observations of gravitational lenses—beyond the Solar System and the Local Group—began decades later in the late twentieth century [2–4]. The ground-breaking detections of neutrinos from the Sun and SN1987A occurred on a similar timescale [5–9], marking the dawn of multi-messenger astronomy. This field experienced a spectacular renaissance three decades later when gravitational waves (GWs), and electromagnetic (EM) radiation from gamma rays to radio waves were detected from a binary neutron star (BNS) merger in 2017 [10,11]. The first confirmed discoveries of gravitationally lensed transient sources, and the first detection of neutrinos from an extragalactic source were both achieved in parallel with the early GW discoveries [12–14].The authors acknowledge generous support from The Royal Society for the ‘Multi-messenger Gravitational Lensing’ Theo Murphy Meeting in Manchester, March 2024. G.P.S. acknowledges support from The Royal Society, the Leverhulme Trust and the Science and Technology Facilities Council (grant number ST/X001296/1). M.A.H. acknowledges support from the Science and Technology Facilities Council (grant number ST/L000946/1)
"I leave stuff better than how i find it": a case for Black girl joy
No full textPorcher, KishaGrounded in Black Girlhood Studies, this qualitative case-study examined how Black high school girls characterized and experienced joy both in and out of school, using the framework of Avenues of Joy (AOJ). The hope is that this qualitative case-study will ideally point researchers and practitioners to concrete ways that schooling cultures, policies, and practices can be amended to make schools a more joyous space for students, specifically Black girls. This research extends beyond exclusively academic spaces and is a call for Black people, in particular, to be intentional about the ways that we harness and cultivate joy for Black girls out in the world. The research question that guided my dissertation study was: (1) How do Black girls characterize and experience joy inside and outside of school? Largely for the girls, their characterizations of joy were framed by being unburdened and free, in addition to a sense of recreation. I found that the girls experienced joy in and outside of the context of school primarily through being in community with people that they loved; experiencing emotional safety; and when they found curated spaces to be and matter. Finally, a significant contribution of this Black Girlhood Study is the way in which this work provided a concrete and multi-faceted conceptualization of joy through my Avenues of Joy (AOJ) Framework.University of Delaware, School of EducationPh.D
Financing Public Education in Delaware Data Updates, 2025
No full textEducation is a vital ingredient in the health of an economy and has direct bearing on the quality of the Delaware workforce. Effective spending on public education may increase the quality of the labor market, enhance the productivity and competitiveness of businesses, and render Delaware attractive to current and emerging industries as well as potential employees. Understanding how the public education system currently uses funding provides an insight on how to turn dollars into productive resources in districts, schools, and classrooms. The purpose of the report is to provide a system-wide review of the public education finance system in Delaware. The report offers details about how public education revenue is raised and spent