bluuebunny/crossref_metadata_2025_split huggingface dataset:
Allostery can convert binding free energies into concerted domain motions in enzymes
Authors: Galenkamp Nicole Stéphanie , Zernia Sarah , Van Oppen Yulan B. , van den Noort Marco , Milias-Argeitis Andreas , Maglia Giovanni 
Published: November 2024

Abstract
Enzymatic mechanisms are typically inferred from structural data. However, understanding enzymes require unravelling the intricate dynamic interplay between dynamics, conformational substates, and multiple protein structures. Here, we use single-molecule nanopore analysis to investigate the catalytic conformational changes of adenylate kinase (AK), an enzyme that catalyzes the interconversion of various adenosine phosphates (ATP, ADP, and AMP). Kinetic analysis validated by hidden Markov models unravels the details of domain motions during catalysis. Our findings reveal that allosteric interactions between ligands and cofactor enable converting binding energies into directional conformational changes of the two catalytic domains of AK. These coordinated motions emerged to control the exact sequence of ligand binding and the affinity for the three different substrates, thereby guiding the reactants along the reaction coordinates. Interestingly, we find that about 10% of enzymes show altered allosteric regulation and ligand affinities, indicating that a subset of enzymes folds in alternative catalytically active forms. Since molecules or proteins might be able to selectively stabilize one of the folds, this observation suggests an evolutionary path for allostery in enzymes. In AK, this complex catalytic framework has likely emerged to prevent futile ATP/ADP hydrolysis and to regulate the enzyme for different energy needs of the cell.
An inhibitory segment within G-patch activators tunes Prp43-ATPase activity during ribosome assembly
Authors: Portugal-Calisto Daniela , Geiger Alexander Gregor , Rabl Julius , Vadas Oscar , Oborská-Oplová Michaela , Mazur Jarosław , Richina Federica , Klingauf-Nerurkar Purnima , Michel Erich , Leitner Alexander , Boehringer Daniel , Panse Vikram Govind 
Published: November 2024

AbstractMechanisms by which G-patch activators tune the processive multi-tasking ATP-dependent RNA helicase Prp43 (DHX15 in humans) to productively remodel diverse RNA:protein complexes remain elusive. Here, a comparative study between a herein and previously characterized activators, Tma23 and Pxr1, respectively, defines segments that organize Prp43 function during ribosome assembly. In addition to the activating G-patch, we discover an inhibitory segment within Tma23 and Pxr1, I-patch, that restrains Prp43 ATPase activity. Cryo-electron microscopy and hydrogen-deuterium exchange mass spectrometry show how I-patch binds to the catalytic RecA-like domains to allosterically inhibit Prp43 ATPase activity. Tma23 and Pxr1 contain dimerization segments that organize Prp43 into higher-order complexes. We posit that Prp43 function at discrete locations on pre-ribosomal RNA is coordinated through toggling interactions with G-patch and I-patch segments. This could guarantee measured and timely Prp43 activation, enabling precise control over multiple RNA remodelling events occurring concurrently during ribosome formation.
Emerging insights in senescence: pathways from preclinical models to therapeutic innovations
Authors: Mansfield Luke , Ramponi Valentina , Gupta Kavya , Stevenson Thomas , Mathew Abraham Binoy , Barinda Agian Jeffilano , Herbstein Florencia , Morsli Samir 
Published: November 2024

AbstractSenescence is a crucial hallmark of ageing and a significant contributor to the pathology of age-related disorders. As committee members of the young International Cell Senescence Association (yICSA), we aim to synthesise recent advancements in the identification, characterisation, and therapeutic targeting of senescence for clinical translation. We explore novel molecular techniques that have enhanced our understanding of senescent cell heterogeneity and their roles in tissue regeneration and pathology. Additionally, we delve into in vivo models of senescence, both non-mammalian and mammalian, to highlight tools available for advancing the contextual understanding of in vivo senescence. Furthermore, we discuss innovative diagnostic tools and senotherapeutic approaches, emphasising their potential for clinical application. Future directions of senescence research are explored, underscoring the need for precise, context-specific senescence classification and the integration of advanced technologies such as machine learning, long-read sequencing, and multifunctional senoprobes and senolytics. The dual role of senescence in promoting tissue homoeostasis and contributing to chronic diseases highlights the complexity of targeting these cells for improved clinical outcomes.
Neural network potential for dislocation plasticity in ceramics
Authors: Zhang Shihao , Li Yan , Suzuki Shuntaro , Nakamura Atsutomo , Ogata Shigenobu 
Published: November 2024

AbstractDislocations in ceramics are increasingly recognized for their promising potential in applications such as toughening intrinsically brittle ceramics and tailoring functional properties. However, the atomistic simulation of dislocation plasticity in ceramics remains challenging due to the complex interatomic interactions characteristic of ceramics, which include a mix of ionic and covalent bonds, and highly distorted and extensive dislocation core structures within complex crystal structures. These complexities exceed the capabilities of empirical interatomic potentials. Therefore, constructing neural network potentials (NNPs) emerges as the optimal solution. Yet, creating a training dataset that includes dislocation structures proves difficult due to the complexity of their core configurations in ceramics and the computational demands of density functional theory for large atomic models containing dislocation cores. In this work, we propose a training dataset from properties that are easier to compute via high-throughput calculation. Using this dataset, we have successfully developed NNPs for dislocation plasticity in ceramics, specifically for three typical functional ceramics: ZnO, GaN, and SrTiO3. These NNPs effectively capture the nonstoichiometric and charged core structures and slip barriers of dislocations, as well as the long-range electrostatic interactions between charged dislocations. The effectiveness of this dataset was further validated by measuring the similarity and uncertainty across snapshots derived from large-scale simulations, alongside extensive validation across various properties. Utilizing the constructed NNPs, we examined dislocation plasticity in ceramics through nanopillar compression and nanoindentation, which demonstrated excellent agreement with experimental observations. This study provides an effective framework for constructing NNPs that enable the detailed atomistic modeling of dislocation plasticity, opening new avenues for exploring the plastic behavior of ceramics.
Awake versus asleep deep brain stimulation targeting the caudal zona incerta for essential tremor
Authors: Stenmark Persson Rasmus , Blomstedt Yulia , Fytagoridis Anders , Hariz Marwan , Blomstedt Patric 
Published: November 2024

AbstractTo compare awake and asleep deep brain stimulation (DBS) surgery for Essential Tremor (ET), we conducted this retrospective cohort study of patients consecutively operated with DBS targeting the caudal Zona incerta (cZi). 37 underwent surgery awake and 55 asleep. Tremor before surgery and on/off stimulation one year after surgery were evaluated using the Essential Tremor Rating Scale (ETRS). Procedural time, electrode localization, stimulation parameters and adverse events were noted and compared. ETRS scores were similar at baseline between the groups except for contralateral arm tremor, which was slightly worse in the awake group. Total ETRS, contralateral arm tremor and activities of daily living scores showed no significant difference between the groups on-stimulation at one-year follow-up. Compared to the awake group, the asleep group had shorter procedural time and lower stimulation parameters. There were no intracranial haemorrhages nor surgery site-infections. Both groups showed a good improvement of tremor at one-year follow-up. Image-guided DBS surgery targeting the cZi enables safe and efficient asleep surgery for ET.
CelloType: a unified model for segmentation and classification of tissue images
Authors: Pang Minxing , Roy Tarun Kanti , Wu Xiaodong , Tan Kai 
Published: November 2024

Abstract
Cell segmentation and classification are critical tasks in spatial omics data analysis. Here we introduce CelloType, an end-to-end model designed for cell segmentation and classification for image-based spatial omics data. Unlike the traditional two-stage approach of segmentation followed by classification, CelloType adopts a multitask learning strategy that integrates these tasks, simultaneously enhancing the performance of both. CelloType leverages transformer-based deep learning techniques for improved accuracy in object detection, segmentation and classification. It outperforms existing segmentation methods on a variety of multiplexed fluorescence and spatial transcriptomic images. In terms of cell type classification, CelloType surpasses a model composed of state-of-the-art methods for individual tasks and a high-performance instance segmentation model. Using multiplexed tissue images, we further demonstrate the utility of CelloType for multiscale segmentation and classification of both cellular and noncellular elements in a tissue. The enhanced accuracy and multitask learning ability of CelloType facilitate automated annotation of rapidly growing spatial omics data.
Microbial Metagenomes Across a Complete Phytoplankton Bloom Cycle: High-Resolution Sampling Every 4 Hours Over 22 Days
Authors: Nunn Brook L. , Timmins-Schiffman Emma , Mudge Miranda C. , Plubell Deanna L. , Chebli Gabriella , Kubanek Julia , Riffle Michael , Noble William S. , Harvey Elizabeth , Nunn Tasman A. , Rynearson Tatiana , Huntemann Marcel , LaButti Kurt , Foster Brian , Foster Bryce , Roux Simon , Palaniappan Krishnaveni , Mukherjee Supratim , Reddy T. B. K. , Daum Chris , Copeland Alex , Chen I-Min A. , Ivanova Natalia N. , Kyrpides Nikos C. , Glavina del Rio Tijana , Eloe-Fadrosh Emiley A. 
Published: November 2024

Abstract
In May and June of 2021, marine microbial samples were collected for DNA sequencing in East Sound, WA, USA every 4 hours for 22 days. This high temporal resolution sampling effort captured the last 3 days of a Rhizosolenia sp. bloom, the initiation and complete bloom cycle of Chaetoceros socialis (8 days), and the following bacterial bloom (2 days). Metagenomes were completed on the time series, and the dataset includes 128 size-fractionated microbial samples (0.22–1.2 µm), providing gene abundances for the dominant members of bacteria, archaea, and viruses. This dataset also has time-matched nutrient analyses, flow cytometry data, and physical parameters of the environment at a single point of sampling within a coastal ecosystem that experiences regular bloom events, facilitating a range of modeling efforts that can be leveraged to understand microbial community structure and their influences on the growth, maintenance, and senescence of phytoplankton blooms.
GAFchromic EBT film lateral resolution and contrast reproduction in the UV-blue range
Authors: Garzella Francesco , Insero Giacomo , Battisti Antonella , Sgarbossa Antonella , Mello Tommaso , Fusi Franco , Romano Giovanni 
Published: November 2024

AbstractThe sensitivity of radiochromic films to UV-blue light is increasingly considered for light dosimetry purposes, owing to their bidimensional detection capabilities and ease of use. While film response to radiation intensity has been widely investigated by commercial scanners, spatial resolution studies remain scarce, especially for small field-of-view applications. These are of growing interest due to the antimicrobial or photo-bio-stimulating effects of UV-blue light sources in in vitro, ex vivo and in vivo models, where precise knowledge of irradiation conditions with adequate spatial resolution is crucial. In this study, we report the spatial lateral resolution and contrast reproduction of GAFchromic EBT2 and EBT3 models. Upon film irradiation by a 405 nm laser source or 365 nm LED, a confocal microscope setup was employed to read the film response at 405, 470, 488, 532 and 570 nm wavelengths, with radiant exposure of 10–70 J/cm2. The measured lateral resolution ranged from 8 to 33 μm. The film capability to reproduce contrast across various spatial frequencies (4–14 lines/mm) was evaluated using modulation transfer function analysis with irradiation performed at 365 nm and 405 nm, revealing a pronounced dependency on both radiant exposure and reading wavelength. These results confirm the film capacity to detect and resolve light intensity variability with a ~ 10 μm resolution, with notable applications in micro-beam profiling and light dosimetry.
HDBind: encoding of molecular structure with hyperdimensional binary representations
Authors: Jones Derek , Zhang Xiaohua , Bennion Brian J. , Pinge Sumukh , Xu Weihong , Kang Jaeyoung , Khaleghi Behnam , Moshiri Niema , Allen Jonathan E. , Rosing Tajana S. 
Published: November 2024

AbstractTraditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.
Exploiting activation radiation from neutron tomography reveals the hidden elemental composition of 3D art objects for free
Authors: Li Yueer , Creange Sara , Zhou Zhou , Southworth William , Pappot Arie , van Eijck Lambert 
Published: November 2024

AbstractNeutron tomography is gaining popularity particularly in cultural heritage research, for non-destructively analysing the inner structure of bulk metal artefacts, such as bronzes, but the induced temporary decay radiation is often considered as a drawback. However, this delayed gamma-emission can be put to good use: by performing gamma spectroscopy after neutron tomography, the interior elemental composition of artefacts can be obtained “for free”. Inspired by this, we propose a ray-tracing approach to non-invasively quantify both interior geometry and elemental composition using only a single neutron tomography experiment. This strategy aligns well with both the aim for efficient use of neutron beam time and the expectation from curators and conservators for minimal neutron irradiation. Here, we outline the core principle of this method, demonstrate the extent of its quantification capability on bulk objects of known composition by fusing neutron tomography and delayed-gamma spectroscopy data sets. We also showcase its practical application on an ancient solid-cast Indonesian bronze statuette, by which we gain insights into how the pristine inner bronze segregated into a different composition than the surrounding shell. Similarly, the method allows us to quantify the composition of a hidden offering in the statuette that consecrates the bronze for worship purposes.
The importance of boundary evolution for solar-wind modelling
Authors: Owens Mathew J. , Barnard Luke , Arge Charles N. 
Published: November 2024

AbstractThe solar wind is a continual outflow of plasma and magnetic field from the Sun’s upper atmosphere—the corona—that expands to fills the solar system. Variability in the near-Earth solar-wind conditions can produce adverse space weather that impacts ground- and space-based technologies. Consequently, numerical fluid models of the solar wind are used to forecast conditions a few days ahead. The solar-wind inner-boundary conditions are supplied by models of the corona that are, in turn, constrained by observations of the photospheric magnetic field. While solar eruptions—coronal mass ejections (CMEs)—are treated as time-dependent structures, a single coronal “snapshot” is typically used to determine the ambient solar-wind for a complete model run. Thus, all available time-history information from previous coronal-model solutions is discarded and the solar wind is treated as a steady-state flow, unchanging in the rotating frame of the Sun. In this study, we use 1 year of daily-updated coronal-model solutions to comprehensively compare steady-state solar-wind modelling with a time-dependent method. We demonstrate, for the first time, how the SS approach can fundamentally misrepresent the accuracy of coronal models. We also attribute three key problems with current space-weather forecasting directly to the steady-state approach: (1) the seemingly paradoxical result that forecasts based on observations from 3-days previous are more accurate than forecasts based on the most recent observations; (2) high inconsistency, with forecasts for a given day jumping significantly as new observations become available, changing CME propagation times by up to 17 h; and (3) insufficient variability in the heliospheric magnetic field, which controls solar energetic particle propagation to Earth. The time-dependent approach is shown to alleviate all three issues. It provides a consistent, physical solution which more accurately represents the information present in the coronal models. By incorporating the time history in the solar wind along the Sun-Earth line, the time-dependent approach will provide improvements to forecasting CME propagation to Earth.
Prospective appraisal of clinical diagnostic algorithms for hepatocellular carcinoma surveillance in Chinese patients with chronic hepatitis B infection
Authors: Chan Henry L. Y. , Hu Yao , Malinowsky Katharina , Madin Kairat , Kroeniger Konstantin , Hou Jinlin , Sharma Ashish 
Published: November 2024

AbstractHepatocellular carcinoma (HCC) is often detected at advanced stages among patients with hepatitis B virus (HBV), underscoring the urgency for more precise surveillance tests. Here, we compare the clinical performance of the novel - GAAD (gender [biological sex], age, alpha-fetoprotein [AFP], protein-induced by vitamin K absence-II [PIVKA-II]) and GALAD (gender [biological sex], age, AFP, Lens-culinaris AFP [AFP-L3]), PIVKA-II) algorithms to assess the utility of AFP-L3 for distinguishing HCC from benign chronic liver disease (CLD) in Chinese patients with predominantly chronic HBV infection. Eligible adults were enrolled, and biomarkers were measured using Elecsys (Cobas) or µTASWAKO assays. In total, 411 participants provided serum samples (HCC, n = 176 [early-stage, n = 110]; CLD, n = 136; specificity n = 101). HBV was the underlying disease etiology for most participants (HCC, 95%; benign CLD, 72%). For GAAD (Cobas), GALAD (Cobas), and GALAD (µTASWAKO), AUCs were 93.1% (95% CI: 90.0–96.2), 93.2% (90.0–96.3), and 92.7% (88.4–96.9) for early-stage, and 95.6% (93.6–97.6), 95.6% (93.6–97.7), and 95.8% (93.2–98.3) for all-stage HCC, versus CLD, respectively. Interestingly, both GAAD and GALAD algorithms demonstrated comparable diagnostic performance regardless of disease etiology (HBV vs. non-HBV), presence of cirrhosis, geographic region, and within pan-tumor specificity panels (p < 0.001), indicating AFP-L3 may have a negligible role in HCC surveillance.
RLS-associated MEIS transcription factors control distinct processes in human neural stem cells
Authors: Kittke Volker , Zhao Chen , Lam Daniel D. , Harrer Philip , Krezel Wojciech , Schormair Barbara , Oexle Konrad , Winkelmann Juliane 
Published: November 2024

Abstract

MEIS1 and MEIS2 encode highly conserved homeodomain transcription factors crucial for developmental processes in a wide range of tissues, including the brain. They can execute redundant functions when co-expressed in the same cell types, but their roles during early stages of neural differentiation have not been systematically compared. By separate knockout and overexpression of MEIS1 and MEIS2 in human neural stem cells, we find they control specific sets of target genes, associated with distinct biological processes. Integration of DNA binding sites with differential transcriptomics implicates MEIS1 to co-regulate gene expression by interaction with transcription factors of the SOX and FOX families. MEIS1 harbors the strongest risk factor for restless legs syndrome (RLS). Our data suggest that MEIS1 can directly regulate the RLS-associated genes NTNG1, MDGA1 and DACH1, constituting new approaches to study the elusive pathomechanism or RLS.
Theoretical study on the prediction of optical properties and thermal stability of fullerene nanoribbons
Authors: Bai Haonan , Gai Xinwen , Sun Lulu , Ma Ji 
Published: November 2024

AbstractIn this work, we predicted two different configurations of fullerene nanoribbons (quasi-hexagonal phase (qHP) and quasi-tetragonal phase (qTP)) based on two-dimensional fullerenes, with widths of 1, 2, and 3 fullerene units, respectively. Based on first-principles calculations and ab-initio molecular dynamics (AIMD), the thermal stability and optical properties of six fullerene nanoribbons were predicted. AIMD studies indicate that wider qHP nanoribbons (qHPs) exhibit better thermal stability, while increased temperatures lead to greater instability. In contrast, qHP-3 shows the best thermal stability among the six structures. Then, the optical gap between the calculated and experimental quasi-hexagonal two-dimensional fullerenes is compared to illustrate the accuracy of the calculation. The absorption spectra of six fullerene nanoribbons were calculated and the anisotropy of light absorption was analyzed. Finally, the charge transfer modes of each excited state were visualized through electron-hole density plots. This work provides an essential theoretical foundation for understanding new all-carbon materials, specifically fullerenes.