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At least 145 records · Page 8

Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins

Here, we outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self-organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure-function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.

59 BASIC BIOLOGICAL SCIENCES↗

Mechanical morphotype switching as an adaptive response in mycobacteria

Invading microbes face a myriad of cidal mechanisms of phagocytes that inflict physical damage to microbial structures. How intracellular bacterial pathogens adapt to these stresses is not fully understood. Here, we report the discovery of a virulence mechanism by which changes to the mechanical stiffness of the mycobacterial cell surface confer refraction to killing during infection. Long-term time-lapse atomic force microscopy was used to reveal a process of “mechanical morphotype switching” in mycobacteria exposed to host intracellular stress. A “soft” mechanical morphotype switch enhances tolerance to intracellular macrophage stress, including cathelicidin. Both pharmacologic treatment, with bedaquiline, and a genetic mutant lacking uvrA modified the basal mechanical state of mycobacteria into a soft mechanical morphotype, enhancing survival in macrophages. Our study proposes microbial cell mechanical adaptation as a critical axis for surviving host-mediated stressors.

59 BASIC BIOLOGICAL SCIENCES↗

Predicting interface structure using the minima hopping method

Here, we adapt the minima hopping method (MHM) to the problem of interfacial structure prediction and apply it to study a canonical problem, the tilt grain boundaries in SrTiO 3 . Our method employs a hybrid approach by first exploring the potential energy surface (PES) of different grain boundary samplings with an empirical force field, among which the fifteen candidates with lower energies are then refined using ab initio density functional theory (DFT) calculations. During the exploratory stage, we bias the search using a local order parameter to primarily sample various reconstructions in the vicinity of the interface, while preserving the crystallinity of the bulk regions. We further enhance the search by incorporating initial structures with rigid body displacements to account for translational variations between bulk phases, enabling the MHM to effectively generate both stoichiometric and nonstoichiometric SrTiO 3 Σ⁢3(111)[110] and Σ⁢3(112)[110] grain boundaries. From an algorithmic standpoint, MHM outperforms earlier studies based on genetic algorithms (GA) by identifying more stable interfacial structures of several SrTiO 3 grain boundaries. The performance of the present implementation of the MHM approach is primarily limited by exploring an approximate description of the PES with a rather simple Buckingham potential. This limitation leads to variations in performance when compared to approaches utilizing more advanced surrogate PES models, such as direct DFT-PES sampling or GA with the embedded atom method (EAM). Despite the present limitations, the MHM approach is able to yield interfacial structures with comparable or lower interfacial energies in specific cases, such as Σ⁢3(111)[110] Γ=1, ±0.5 and Σ⁢3(112)[110] Γ= ±1, −2, underscoring the robustness of the MHM approach even with a simple approximation of the DFT PES. The MHM interfacial structure prediction method thus offers an efficient approach to understanding the grain boundaries and heterointerfaces at the atomic scale, providing an important prerequisite for effective materials design.

density functional theory↗

The gut microbiome mediates adaptation to scarce food in Coleoptera

Beetles are ubiquitous cave invertebrates worldwide that adapted to scarce subterranean resources when they colonized caves. Here, we investigated the potential role of gut microbiota in the adaptation of beetles to caves from different climatic regions of the Carpathians. The beetles’ microbiota was host-specific, reflecting phylogenetic and nutritional adaptation. The microbial community structure further resolved conspecific beetles by caves suggesting microbiota-host coevolution and influences by local environmental factors. The detritivore species hosted a variety of bacteria known to decompose and ferment organic matter, suggesting turnover and host cooperative digestion of the sedimentary microbiota and allochthonous-derived nutrients. The cave Carabidae, with strong mandibula, adapted to predation and scavenging of animal and plant remains, had distinct microbiota dominated by symbiotic lineages Spiroplasma or Wolbachia. All beetles had relatively high levels of fermentative Carnobacterium and Vagococcus involved in lipid accumulation and a reduction of metabolic activity, and both features characterize adaptation to caves.

59 BASIC BIOLOGICAL SCIENCES↗

Population genomics provides insights into the genetic basis of adaptive evolution in the mushroom-forming fungus Lentinula edodes

Introduction: Mushroom-forming fungi comprise diverse species that develop complex multicellular structures. In cultivated species, both ecological adaptation and artificial selection have driven genome evolution. However, little is known about the connections among genotype, phenotype and adaptation in mushroom-forming fungi. Objectives: This study aimed to (1) uncover the population structure and demographic history of Lentinula edodes, (2) dissect the genetic basis of adaptive evolution in L. edodes, and (3) determine if genes related to fruiting body development are involved in adaptive evolution. Methods: We analyzed genomes and fruiting body-related traits (FBRTs) in 133 L. edodes strains and conducted RNA-seq analysis of fruiting body development in the YS69 strain. Combined methods of genomic scan for divergence, genome-wide association studies (GWAS), and RNA-seq were used to dissect the genetic basis of adaptive evolution. Results: We detected three distinct subgroups of L. edodes via single nucleotide polymorphisms, which showed robust phenotypic and temperature response differentiation and correlation with geographical distribution. Demographic history inference suggests that the subgroups diverged 36,871 generations ago. Moreover, L. edodes cultivars in China may have originated from the vicinity of Northeast China. A total of 942 genes were found to be related to genetic divergence by genomic scan, and 719 genes were identified to be candidates underlying FBRTs by GWAS. Integrating results of genomic scan and GWAS, 80 genes were detected to be related to phenotypic differentiation. A total of 364 genes related to fruiting body development were involved in genetic divergence and phenotypic differentiation. Conclusion: Adaptation to the local environment, especially temperature, triggered genetic divergence and phenotypic differentiation of L. edodes. A general model for genetic divergence and phenotypic differentiation during adaptive evolution in L. edodes, which involves in signal perception and transduction, transcriptional regulation, and fruiting body morphogenesis, was also integrated here.

59 BASIC BIOLOGICAL SCIENCES↗

A Dynamic Hierarchical Attention Framework for Multimodal Malware Detection

The increasing use of Android in the worldwide mobile ecosystem has come along with a significant increase in advanced malware, highlighting the critical necessity for efficient, scalable, and adaptable detection systems. Despite recent advancements in machine learning improving malware detection, the majority of current solutions are limited to one, two, or three data modalities, hence neglecting the comprehensive behavioral spectrum of contemporary multi-vector threats. This thesis presents the first comprehensive multimodal framework for Android malware detection, which combines textual, time-series (temporal), graph-based (structural), and visual information using an innovative hierarchical attention mechanism and Dynamic Fusion Controller (DFC). Our methodology consistently classifies and processes modalities as either sequential or structural, facilitating content-adaptive weighting and resilient cross-modal representation learning. We advance the implementation of cutting-edge time series techniques, such as MiniRocket, for malware detection, hence creating new opportunities for temporal analysis in cybersecurity. Comprehensive experimental assessment shows that our framework performs exceptionally well, with 99.46% classification accuracy and 97.15% detection accuracy, significantly outperforming existing approaches through effective multimodal integration and hierarchical attention mechanisms.

Nazmin, Tamanna↗

Edgewise Structural Damping of a 2.8-MW Land-Based Wind Turbine Rotor Blade

Modern wind turbines push the predictive capabilities of state-of-the-art aero-servo-elastic tools. The existing limitations hide across the numerical tool chain and can result in serious issues, such as missing aeroelastic instabilities during the design phase. Structural damping is an input that is usually hard to estimate, but also has a major impact on the turbine behavior. In this paper, we discuss an experiment that aims to accurately quantify the structural damping characterizing the edgewise modes of modern wind turbine blades. The experiment is carried out on a 2.8-MW land-based wind turbine and features a fast yaw actuation that induces an edgewise motion on one of the three blades. The Covariant-subspace system identification (Cov-SSI) method is then used to post-process the blade root moment to estimate the short-term edgewise structural damping. Despite limitations of the Cov-SSI method, which consistently under-predicts the absolute values of damping, we observe that structural damping decreases across the first three blade edgewise modes, which is different from the stiffness-proportional damping model that assumes that structural damping increases with the modes. This paper argues that a stiffness-proportional damping model, which is implemented in most aeroelastic tools, is therefore not conservative and might hide aeroelastic instabilities that can instead appear in the field.

17 WIND ENERGY↗

Comparing crystal structures with symmetry and geometry

Abstract Measuring the similarity between two arbitrary crystal structures is a common challenge in crystallography and materials science. Although there are an infinite number of ways to mathematically relate two crystal structures, only a few are physically meaningful. Here we introduce both a geometry-based and a symmetry-adapted similarity metric to compare crystal structures. Using crystal symmetry and combinatorial optimization we describe an algorithm to arrive at the structural relationship that minimizes these similarity metrics across all possible maps between any pair of crystal structures. The approach makes it possible to (i) identify pairs of crystal structures that are identical, (ii) quantitatively measure the similarity between crystal structures, and (iii) find and rank structural transformation pathways between any pair of crystal structures. We discuss the advantages of using the symmetry-adapted cost metric over the geometric cost. Finally, we show that all known structural transformation pathways between common crystal structures are recovered with the mapping algorithm. The methodology presented in this study will be of value to efforts that seek to catalogue crystal structures, identify structural transformation pathways or prune large first-principles datasets used to parameterize on-lattice Hamiltonians.

36 MATERIALS SCIENCE↗

Come Hell or High-Water: Challenges for Adapting Pacific Northwest Water Law

The Pacific Northwest region of the United States has been recognized as a leader in crafting water laws that work to balance human needs and ecological considerations. However, this region is experiencing changing dynamics that test the strength of existing water policies and laws. Such dynamics include increasing populations, new and exempt uses, quantification of tribal treaty rights, species protection, renegotiation of the Columbia River Treaty, and the impacts of a changing climate. Together, these dynamics are stressing the legal framework, which remains vital to ensuring sustainable water supplies now and into the future. Here, the history behind water resources management in Oregon, Washington, Idaho, and Montana provides the foundation for the laws in place today. Although all four states share regulatory features, nuances specific to each state have tremendously affected their governance structures and ability to adapt to changing dynamics. Although a challenge to meet these changing dynamics, collaborative efforts offer creative opportunities for meeting current and future needs.

99 GENERAL AND MISCELLANEOUS↗

Whole-soil warming decreases abundance and modifies the community structure of microorganisms in the subsoil but not in surface soil

Abstract. The microbial community composition in subsoils remains understudied, and it is largely unknown whether subsoil microorganisms show a similar response to global warming as microorganisms at the soil surface do. Since microorganisms are the key drivers of soil organic carbon decomposition, this knowledge gap causes uncertainty in the predictions of future carbon cycling in the subsoil carbon pool (> 50 % of the soil organic carbon stocks are below 30 cm soil depth). In the Blodgett Forest field warming experiment (California, USA) we investigated how +4 ∘C warming in the whole-soil profile to 100 cm soil depth for 4.5 years has affected the abundance and community structure of microorganisms. We used proxies for bulk microbial biomass carbon (MBC) and functional microbial groups based on lipid biomarkers, such as phospholipid fatty acids (PLFAs) and branched glycerol dialkyl glycerol tetraethers (brGDGTs). With depth, the microbial biomass decreased and the community composition changed. Our results show that the concentration of PLFAs decreased with warming in the subsoil (below 30 cm) by 28 % but was not affected in the topsoil. Phospholipid fatty acid concentrations changed in concert with soil organic carbon. The microbial community response to warming was depth dependent. The relative abundance of Actinobacteria increased in warmed subsoil, and Gram+ bacteria in subsoils adapted their cell membrane structure to warming-induced stress, as indicated by the ratio of anteiso to iso branched PLFAs. Our results show for the first time that subsoil microorganisms can be more affected by warming compared to topsoil microorganisms. These microbial responses could be explained by the observed decrease in subsoil organic carbon concentrations in the warmed plots. A decrease in microbial abundance in warmed subsoils might reduce the magnitude of the respiration response over time. The shift in the subsoil microbial community towards more Actinobacteria might disproportionately enhance the degradation of previously stable subsoil carbon, as this group is able to metabolize complex carbon sources.

54 ENVIRONMENTAL SCIENCES↗

Pathways to IEC Standards for Heliostat Design Qualification and Site Acceptance in Central Receiver CSP Applications

This paper surveys the existing landscape of standards relevant to heliostats, identifies their gaps, and proposes a path forward to a comprehensive set of heliostat guidelines, technical specifications, and standards under the framework of International Electrotechnical Commission (IEC) TC 117. Gaps in existing guidelines and standards are surveyed using a three-tiered taxonomy: component-level, heliostat-level, and field-level. At each level, the gap analysis is followed by a proposal for a coordinated path forward on the development of standards. At the component level, advances in the understanding of wind loading should inform a technical specification for drives and structures. Reflectors require consolidation of measurement guidelines into existing standards documents. Communications & controls require technical standards to inform their selection and secure implementation. At the heliostat level, IEC 62817 (solar trackers) adequately characterizes drive systems, structures, and electronics, but requires adaptation to heliostats' use patterns, operating modes, and expected life cycles. IEC 62817 does not address heliostat beam quality and pointing accuracy, but the process for determining both is elaborated in the SolarPACES Guideline for Heliostat Performance Testing. This SolarPACES document requires two main modifications: adaptation to IEC language and inclusion of testing after heliostats which have undergone accelerated weathering and mechanical cycling (to understand performance degradation). At the field level, IEC 62862-4-2 addresses the function and control of heliostat fields but does not cover the statistically rigorous testing of heliostat groups, or field performance factors like security and soiling. The addition of documents under IEC-62862-4 is proposed to address this gap.

beam↗

Improving robustness for model discerning synthesis process of uranium oxide with unsupervised domain adaptation

The quantitative characterization of surface structures captured in scanning electron microscopy (SEM) images has proven to be effective for discerning provenance of an unknown nuclear material. Recently, many works have taken advantage of the powerful performance of convolutional neural networks (CNNs) to provide faster and more consistent characterization of surface structures. However, one inherent limitation of CNNs is their degradation in performance when encountering discrepancy between training and test datasets, which limits their use widely. The common discrepancy in an SEM image dataset occurs at low-level image information due to user-bias in selecting acquisition parameters and microscopes from different manufacturers. Therefore, in this study, we present a domain adaptation framework to improve robustness of CNNs against the discrepancy in low-level image information. Furthermore, our proposed approach makes use of only unlabeled test samples to adapt a pretrained model, which is more suitable for nuclear forensics application for which obtaining both training and test datasets simultaneously is a challenge due to data sensitivity. Through extensive experiments, we demonstrate that our proposed approach effectively improves the performance of a model by at least 18% when encountering domain discrepancy, and can be deployed in many CNN architectures.

scanning electron microscopy↗

Evaluation Toolkit for Technical Assistance Programs

Across the United States, every home, business, industry, and government depends on abundant, reliable, and affordable electricity. Energy stakeholders have many options to improve how energy is generated, distributed, and used, but choosing the right path can be complex and challenging. To support more informed decision-making about local electricity systems, the U.S. Department of Energy (DOE) and its national laboratories provide customized technical assistance (TA) through several different programs. These TA programs are designed to support a wide range of stakeholders with a variety of needs. Technical assistance may include brief consultations with subject-matter experts, in-depth technical modeling and analysis, stakeholder engagement, and peer-to-peer exchange. Delivering effective TA is an iterative process that requires evaluation and adaptation. A comprehensive and robust evaluation framework provides the structure needed to ensure that TA programs and practitioners remain effective, responsive to industry trends, and aligned with local needs. This toolkit provides a framework—including a clear, structured process—that TA program staff can adapt to their program's goals to evaluate success.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Fungal diversity and function in metagenomes sequenced from extreme environments

Fungi are increasingly recognized as key players in various extreme environments. Here we present an analysis of publicly-sourced metagenomes from global extreme environments, focusing on fungal taxonomy and function. The majority of 855 selected metagenomes contained scaffolds assigned to fungi. Relative abundance of fungi was as high as 10% of protein-coding genes with taxonomic annotation, with up to 289 fungal genera per sample. Despite taxonomic clustering by environment, fungal communities were more dissimilar than archaeal and bacterial communities, both for within- and between-environment comparisons. Relatively abundant fungal classes in extreme environments included Dothideomycetes, Eurotiomycetes, Leotiomycetes, Pezizomycetes, Saccharomycetes, and Sordariomycetes. Broad generalists and prolific aerial spore formers were the most relatively abundant fungal genera detected in most of the extreme environments, bringing up the question of whether they are actively growing in those environments or just surviving as spores. More specialized fungi were common in some environments, such as zoosporic taxa in cryosphere water and hot springs. Relative abundances of genes involved in adaptation to general, thermal, oxidative, and osmotic stress were greatest in soda lake, acid mine drainage, and cryosphere water samples.

60 APPLIED LIFE SCIENCES↗

Structure of the respiratory MBS complex reveals iron-sulfur cluster catalyzed sulfane sulfur reduction in ancient life

Abstract Modern day aerobic respiration in mitochondria involving complex I converts redox energy into chemical energy and likely evolved from a simple anaerobic system now represented by hydrogen gas-evolving hydrogenase (MBH) where protons are the terminal electron acceptor. Here we present the cryo-EM structure of an early ancestor in the evolution of complex I, the elemental sulfur (S 0 )-reducing reductase MBS. Three highly conserved protein loops linking cytoplasmic and membrane domains enable scalable energy conversion in all three complexes. MBS contains two proton pumps compared to one in MBH and likely conserves twice the energy. The structure also reveals evolutionary adaptations of MBH that enabled S 0 reduction by MBS catalyzed by a site-differentiated iron-sulfur cluster without participation of protons or amino acid residues. This is the simplest mechanism proposed for reduction of inorganic or organic disulfides. It is of fundamental significance in the iron and sulfur-rich volcanic environments of early earth and possibly the origin of life. MBS provides a new perspective on the evolution of modern-day respiratory complexes and of catalysis by biological iron-sulfur clusters.

59 BASIC BIOLOGICAL SCIENCES↗

A sorghum pangenome reference improves global crop trait discovery

Although the green revolution adapted a handful of crops to homogeneous and high-input industrialized agriculture, much of the global population still relies on the local production of variable crop cultivars by low-input smallholder farms. This diversity of unhomogenized crops, like that of the grain and bioenergy crop sorghum, offers raw materials for genetic gain and cultivar improvement. However, breeding efforts can be constrained by highly specialized traits and breeding targets Here, to bridge this diversity, we constructed a 33-member pangenome reference and a diversity panel across 1,984 cultivars and landraces. We leveraged these resources to explore the complex interplay among historical contingency, ongoing adaptation and previously uncharacterized structural diversity. Specifically, our analyses conclusively demonstrated multiple nested and deeply diverged structural variants in the domestication gene SHATTERING1, which distinguish the previously established multicentric origin of sorghum. We then applied landscape genomics to reveal how gene flow and secondary contact created the complex genetic mosaic in contemporary breeding networks. As proof of concept for pangenome-accelerated trait discovery, we connected biosynthetic gene cluster structural variation to phenotypic leaf concentration of the cyanogenic glucoside dhurrin. Combined, these approaches will accelerate breeding and trait discovery and provide a framework for similar applications in other crops.

agricultural genetics↗

Crystal and magnetic structures of magnetic topological insulators MnBi 2 Te 4 and MnBi 4 Te 7

Using single-crystal neutron diffraction, we present a systematic investigation of the crystal structure and magnetism of the van der Waals topological insulators MnBi 2 Te 4 and MnBi 4 Te 7 , where rich topological quantum states have been recently predicted and observed. Structural refinements reveal that considerable Bi atoms occupied on the Mn sites in both materials, distinct from the previously reported antisite disorder. We show unambiguously that MnBi 2 Te 4 orders antiferromagnetically below 24 K, featured by a magnetic symmetry R I - 3 c , while MnBi 4 Te 7 is antiferromagnetic below 13 K with a magnetic space group P c - 3 c 1 . They both present antiferromagnetically coupled ferromagnetic layers with spins along the c axis. We put forward a stacking rule for the crystal structure of an infinitely adaptive series MnBi 2 n Te 3 n + 1 ( n ≥ 1 ) with a building unit of [ Bi 2 Te 3 ] . Overall, by comparing the magnetic properties between MnBi 2 Te 4 and MnBi 4 Te 7 , together with recent density-functional theory calculations, we concluded that a two-dimensional magnetism limit might be realized in the derivatives. Our work may promote theoretical studies of topological magnetic states in the series of MnBi 2 n Te 3 n + 1 .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

AMReX and pyAMReX: Looking beyond the exascale computing project

AMReX is a software framework for the development of block-structured mesh applications with adaptive mesh refinement (AMR). AMReX was initially developed and supported by the AMReX Co-Design Center as part of the U.S. DOE Exascale Computing Project (ECP), and is continuing to grow post-ECP. In addition to adding new functionality and performance improvements to the core AMReX framework, we have also developed a Python binding, pyAMReX, that provides a bridge between AMReX-based application codes and the data science ecosystem. pyAMReX provides zero-copy application GPU data access for AI/ML, in situ analysis and application coupling, and enables rapid, massively parallel prototyping. In this paper we review the overall functionality of AMReX and pyAMReX, focusing on new developments, new functionality, and optimizations of key operations. We also summarize capabilities of ECP projects that used AMReX and provide an overview of new, non-ECP applications.

Myers, Andrew↗