bluuebunny/crossref_metadata_2025_split huggingface dataset:
The morphospace of the brain-cognition organisation
Authors: Pacella Valentina , Nozais Victor , Talozzi Lia , Abdallah Majd , Wassermann Demian , Forkel Stephanie J. , Thiebaut de Schotten Michel 
Published: September 2024

AbstractOver the past three decades, functional neuroimaging has amassed abundant evidence of the intricate interplay between brain structure and function. However, the potential anatomical and experimental overlap, independence, granularity, and gaps between functions remain poorly understood. Here, we show the latent structure of the current brain-cognition knowledge and its organisation. Our approach utilises the most comprehensive meta-analytic fMRI database (Neurosynth) to compute a three-dimensional embedding space–morphospace capturing the relationship between brain functions as we currently understand them. The space structure enables us to statistically test the relationship between functions expressed as the degree to which the characteristics of each functional map can be anticipated based on its similarities with others–the predictability index. The morphospace can also predict the activation pattern of new, unseen functions and decode thoughts and inner states during movie watching. The framework defined by the morphospace will spur the investigation of novel functions and guide the exploration of the fabric of human cognition.
A structural rationale for reversible vs irreversible amyloid fibril formation from a single protein
Authors: Frey Lukas , Zhou Jiangtao , Cereghetti Gea , Weber Marco E. , Rhyner David , Pokharna Aditya , Wenchel Luca , Kadavath Harindranath , Cao Yiping , Meier Beat H. , Peter Matthias , Greenwald Jason , Riek Roland , Mezzenga Raffaele 
Published: September 2024

AbstractReversible and irreversible amyloids are two diverging cases of protein (mis)folding associated with the cross-β motif in the protein folding and aggregation energy landscape. Yet, the molecular origins responsible for the formation of reversible vs irreversible amyloids have remained unknown. Here we provide evidence at the atomic level of distinct folding motifs for irreversible and reversible amyloids derived from a single protein sequence: human lysozyme. We compare the 2.8 Å structure of irreversible amyloid fibrils determined by cryo-electron microscopy helical reconstructions with molecular insights gained by solid-state NMR spectroscopy on reversible amyloids. We observe a canonical cross-β-sheet structure in irreversible amyloids, whereas in reversible amyloids, there is a less-ordered coexistence of β-sheet and helical secondary structures that originate from a partially unfolded lysozyme, thus carrying a “memory” of the original folded protein precursor. We also report the structure of hen egg-white lysozyme irreversible amyloids at 3.2 Å resolution, revealing another canonical amyloid fold, and reaffirming that irreversible amyloids undergo a complete conversion of the native protein into the cross-β structure. By combining atomic force microscopy, cryo-electron microscopy and solid-state NMR, we show that a full unfolding of the native protein precursor is a requirement for establishing irreversible amyloid fibrils.
Ferromagnetic quantum critical point protected by nonsymmorphic symmetry in a Kondo metal
Authors: Shin Soohyeon , Ramires Aline , Pomjakushin Vladimir , Plokhikh Igor , Pomjakushina Ekaterina 
Published: September 2024

AbstractQuantum critical points (QCPs), zero-temperature phase transitions, are windows to fundamental quantum-mechanical phenomena associated with universal behaviour. Magnetic QCPs have been extensively investigated in the vicinity of antiferromagnetic order. However, QCPs are rare in metallic ferromagnets due to the coupling of the order parameter to electronic soft modes. Recently, antisymmetric spin-orbit coupling in noncentrosymmetric systems was suggested to protect ferromagnetic QCPs. Nonetheless, multiple centrosymmetric materials host FM QCPs, suggesting a more general mechanism behind their protection. In this context, CeSi2-δ, a dense Kondo lattice crystallising in a centrosymmetric structure, exhibits ferromagnetic order when Si is replaced with Ag. We report that the Ag-substitution to CeSi1.97 linearly suppresses the ferromagnetic order towards a QCP, accompanied by concurrent strange-metal behaviour. Herein, we suggest that, despite the centrosymmetric structure, spin-orbit coupling arising from the local noncentrosymmetric structure, in combination with nonsymmorphic symmetry, can protect ferromagnetic QCPs. Our findings offer a general guideline for discovering new ferromagnetic QCPs and highlight one new family of materials within which the interplay of topology and quantum phase transitions can be investigated in the context of strongly correlated systems.
Experience of Euclidean geometry sculpts the development and dynamics of rodent hippocampal sequential cell assemblies
Authors: Farooq Usman , Dragoi George 
Published: September 2024

AbstractEuclidean space is the fabric of the world we live in. Whether and how geometric experience shapes our spatial-temporal representations of the world remained unknown. We deprived male rats of experience with crucial features of Euclidean geometry by rearing them inside spheres, and compared activity of large hippocampal neuronal ensembles during navigation and sleep with that of cuboid cage-reared controls. Sphere-rearing from birth permitted emergence of accurate neuronal ensemble spatial codes and preconfigured and plastic time-compressed neuronal sequences. However, sphere-rearing led to diminished individual place cell tuning, more similar neuronal mapping of different track ends/corners, and impaired pattern separation and plasticity of multiple linear tracks, coupled with reduced preconfigured sleep network repertoires. Subsequent experience with multiple linear environments over four days largely reversed these effects. Thus, early-life experience with Euclidean geometry enriches the hippocampal repertoire of preconfigured neuronal patterns selected toward unique representation and discrimination of multiple linear environments.
Pharmacokinetics and pharmacogenomics of ribociclib in black patients with metastatic breast cancer the LEANORA study
Authors: Schlam Ilana , Smith D. Max , Peer Cody , Sissung Tristan , Schmidt Keith T. , Tan Ming , Chitalia Ami , Bishopric Nanette H. , Steinberg Seth , Choo-Wosoba Hyoyoung , Napoli Giulia , Gallagher Christopher , Ashai Nadia , Whitaker Kristen , Mainor Candace , Tiwari Shruti , Swanson Nicole , Malloy Stacy , Isaacs Claudine , Figg William Douglas , Swain Sandra M. 
Published: September 2024

AbstractUnderrepresented populations’ participation in clinical trials remains limited, and the potential impact of genomic variants on drug metabolism remains elusive. This study aimed to assess the pharmacokinetics (PK) and pharmacogenomics (PGx) of ribociclib in self-identified Black women with hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2) advanced breast cancer. LEANORA (NCT04657679) was a prospective, observational, multicenter cohort study involving 14 Black women. PK and PGx were evaluated using tandem mass spectrometry and PharmacoScan™ microarray (including CYP3A5*3, *6, and *7). CYP3A5 phenotypes varied among participants: 7 poor metabolizers (PM), 6 intermediate metabolizers (IM), and one normal metabolizer (NM). The area under the curve did not significantly differ between PMs (39,230 h*ng/mL) and IM/NMs (43,546 h*ng/mL; p = 0.38). The incidence of adverse events (AEs) was also similar. We found no association between CYP3A5 genotype and ribociclib exposure. Continued efforts are needed to include diverse populations in clinical trials to ensure equitable treatment outcomes.
Integrated modeling to control vaporization-induced composition change during additive manufacturing of nickel-based superalloys
Authors: Mukherjee Tuhin , Shinjo Junji , DebRoy Tarasankar , Panwisawas Chinnapat 
Published: September 2024

AbstractA critical issue in laser powder bed fusion (LPBF) additive manufacturing is the selective vaporization of alloying elements resulting in poor mechanical properties and corrosion resistance of parts. The process also alters the part’s chemical composition compared to the feedstock. Here we present a novel multi-physics modeling framework, integrating heat and fluid flow simulations, thermodynamic calculations, and evaporation modeling to estimate and control the composition change during LPBF of nickel-based superalloys. Experimental validation confirms the accuracy of our model. Moreover, we quantify the relative vulnerabilities of different nickel-based superalloys to composition change quantitatively and we examine the effect of remelting due to the layer-by-layer deposition during the LPBF process. Spatial variations in evaporative flux and compositions for each element were determined, providing valuable insights into the LPBF process and product attributes. The results of this study can be used to optimize the LPBF process parameters such as laser power, scanning speed, and powder layer thickness to ensure the production of high-quality components with desired chemical compositions.
A fast all-optical 3D photoacoustic scanner for clinical vascular imaging
Authors: Huynh N. T. , Zhang E. , Francies O. , Kuklis F. , Allen T. , Zhu J. , Abeyakoon O. , Lucka F. , Betcke M. , Jaros J. , Arridge S. , Cox B. , Plumb A. A. , Beard P. 
Published: September 2024

AbstractThe clinical assessment of microvascular pathologies (in diabetes and in inflammatory skin diseases, for example) requires the visualization of superficial vascular anatomy. Photoacoustic tomography (PAT) scanners based on an all-optical Fabry–Perot ultrasound sensor can provide highly detailed 3D microvascular images, but minutes-long acquisition times have precluded their clinical use. Here we show that scan times can be reduced to a few seconds and even hundreds of milliseconds by parallelizing the optical architecture of the sensor readout, by using excitation lasers with high pulse-repetition frequencies and by exploiting compressed sensing. A PAT scanner with such fast acquisition minimizes motion-related artefacts and allows for the volumetric visualization of individual arterioles, venules, venous valves and millimetre-scale arteries and veins to depths approaching 15 mm, as well as for dynamic 3D images of time-varying tissue perfusion and other haemodynamic events. In exploratory case studies, we used the scanner to visualize and quantify microvascular changes associated with peripheral vascular disease, skin inflammation and rheumatoid arthritis. Fast all-optical PAT may prove useful in cardiovascular medicine, oncology, dermatology and rheumatology.
Recent uplift of Chomolungma enhanced by river drainage piracy
Authors: Han Xu , Dai Jin-Gen , Smith Adam G. G. , Xu Shi-Ying , Liu Bo-Rong , Wang Cheng-Shan , Fox Matthew 
Published: September 2024

Abstract
The Himalayas, which host glaciers, modulate the Indian Monsoon and create an arid Tibetan Plateau, play a vital role in distributing freshwater resources to the world’s most populous regions. The Himalayas formed under prolonged crustal thickening and erosion by glaciers and rivers. Chomolungma (8,849 m)—also known as Mount Everest or Sagarmāthā—is higher than surrounding peaks, and GPS measurements suggest a higher uplift rate in recent years than the long-term trend. Here we analyse the potential contribution of a river capture event in the Kosi River drainage basin on the renewed surface uplift of Chomolungma. We numerically reconstruct the capture process using a simple stream power model combined with nonlinear inverse methods constrained by modern river profiles. Our best-fit model suggests the capture event occurred approximately 89 thousand years ago and caused acceleration of downstream incision rates. Flexural models estimate this non-steady erosion triggers isostatic response and surface uplift over a broad geographical area. We suggest that part of Chomolungma’s anomalous elevation (~15–50 m) can be explained as the isostatic response to capture-triggered river incision, highlighting the complex interplay between geological dynamics and the formation of topographic features.
Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset
Authors: Agraz Melih , Deng Yixiang , Karniadakis George Em , Mantzoros Christos Socrates 
Published: September 2024

AbstractPatients with type 2 diabetes mellitus (T2DM) who have severe hypoglycemia (SH) poses a considerable risk of long-term death, especially among the elderly, demanding urgent medical attention. Accurate prediction of SH remains challenging due to its multifaced nature, contributed from factors such as medications, lifestyle choices, and metabolic measurements. In this study, we propose a systematic approach to improve the robustness and accuracy of SH predictions using machine learning models, guided by clinical feature selection. Our focus is on developing long-term SH prediction models using both semi-supervised learning and supervised learning algorithms. Using the action to control cardiovascular risk in diabetes trial, which includes electronic health records for over 10,000 individuals, we focus on studying adults with T2DM. Our results indicate that the application of a multi-view co-training method, incorporating the random forest algorithm, improves the specificity of SH prediction, while the same setup with Naive Bayes replacing random forest demonstrates better sensitivity. Our framework also provides interpretability of machine learning models by identifying key predictors for hypoglycemia, including fasting plasma glucose, hemoglobin A1c, general diabetes education, and NPH or L insulins. The integration of data routinely available in electronic health records significantly enhances our model’s capability to predict SH events, showcasing its potential to transform clinical practice by facilitating early interventions and optimizing patient management. By enhancing prediction accuracy and identifying crucial predictive features, our study contributes to advancing the understanding and management of hypoglycemia in this population.
Oneyear longitudinal study on biomarkers of blood–brain barrier permeability in COVID-19 patients
Authors: Wallensten Johanna , Havervall Sebastian , Power Yvonne , Åsberg Marie , Borg Kristian , Nager Anna , Thålin Charlotte , Mobarrez Fariborz 
Published: September 2024

AbstractThe pathophysiology behind neurological and cognitive sequelae of COVID-19 may be related to dysfunction of the blood-brain barrier (BBB) and previous research indicate transient neuronal injury and glial activation. The aim of this study was to investigate if COVID-19 is related to increased BBB permeability by analyzing leakage of biomarkers such as astrocyte-derived extracellular vesicles (EVs) and S100B. We also investigated whether levels of these biomarkers correlated with self-reported symptoms that persisted > 2 months. The samples in this 1-year follow-up study came from an ongoing longitudinal study of unvaccinated patients hospitalized for COVID-19 at Danderyd University Hospital, Stockholm, Sweden, between April and June 2020. Blood samples were collected at baseline and 4, 8, and 12 months after hospitalization. Information on self-reported clinical symptoms was collected at follow-up visits. A total of 102 patients were enrolled, and 47 completed all follow-up measurements. Peak levels of both biomarkers were observed at 4 months in the subset of 55 patients who were measured at this timepoint. At 12 months, the biomarkers had returned to baseline levels. The biomarkers were not correlated with any of the long-term self-reported symptoms. COVID-19 is associated with transient increased BBB permeability, shown by elevated levels of astrocyte biomarkers in plasma. However, these levels return to baseline 12 months post-infection and do not correlate with long-term symptoms. Further research is needed to unravel the underlying mechanisms causing long-term symptoms in COVID-19 patients.
Microbial acidification by N, S, Fe and Mn oxidation as a key mechanism for deterioration of subsea tunnel sprayed concrete
Authors: Karačić Sabina , Suarez Carolina , Hagelia Per , Persson Frank , Modin Oskar , Martins Paula Dalcin , Wilén Britt-Marie 
Published: September 2024

AbstractThe deterioration of fibre-reinforced sprayed concrete was studied in the Oslofjord subsea tunnel (Norway). At sites with intrusion of saline groundwater resulting in biofilm growth, the concrete exhibited significant concrete deterioration and steel fibre corrosion. Using amplicon sequencing and shotgun metagenomics, the microbial taxa and surveyed potential microbial mechanisms of concrete degradation at two sites over five years were identified. The concrete beneath the biofilm was investigated with polarised light microscopy, scanning electron microscopy and X-ray diffraction. The oxic environment in the tunnel favoured aerobic oxidation processes in nitrogen, sulfur and metal biogeochemical cycling as evidenced by large abundances of metagenome-assembled genomes (MAGs) with potential for oxidation of nitrogen, sulfur, manganese and iron, observed mild acidification of the concrete, and the presence of manganese- and iron oxides. These results suggest that autotrophic microbial populations involved in the cycling of several elements contributed to the corrosion of steel fibres and acidification causing concrete deterioration.
Achieving liquid processors by colloidal suspensions for reservoir computing
Authors: Fortulan Raphael , Kheirabadi Noushin Raeisi , Chiolerio Alessandro , Adamatzky Andrew 
Published: September 2024

AbstractThe increasing use of machine learning, with its significant computational and environmental costs, has motivated the exploration of unconventional computing substrates. Liquid substrates, such as colloids, are of particular interest due to their ability to conform to various shapes while exhibiting complex dynamics resulting from the collective behaviour of the constituent colloidal particles. This study explores the potential of using a PEDOT:PSS colloidal suspension as a physical reservoir for reservoir computing in spoken digit recognition. Reservoir computing uses high-dimensional dynamical systems to perform tasks with different substrates, including physical ones. Here, a physical reservoir is implemented that encodes temporal data by exploiting the rich dynamics inherent in colloidal suspensions, thus avoiding reliance on conventional computing hardware. The reservoir processes audio input encoded as spike sequences, which are then classified using a trained readout layer to identify spoken digits. Evaluation across different speaker scenarios shows that the colloidal reservoir achieves high accuracy in classification tasks, demonstrating its viability as a physical reservoir substrate.
Spectrum distribution‐based blind calibration method of mismatches for time‐interleaved ADCs
Authors: Zhi He , Wang Hui , Jia Minghao , Xie Huanqing , Wu Jianhui 
Published: September 2024

AbstractThis brief proposes a calibration method for offset, gain, and timing‐skew mismatches in time‐interleaved analog‐to‐digital converters. The calibration method includes the estimation method and the correction method. The estimation method is based on the difference between the spectral distribution of the error harmonics and the input signal. The correction method adopts sequential correction. Simulation results demonstrate that the proposed method calibrated all three mismatches simultaneously and eased the limit for input signals compared with the traditional method.
Structural characterisation for the synthesis of large‐scale combined electric–gas networks
Authors: Birchfield Adam B. , Abu‐Khalifa Yousef 
Published: September 2024

AbstractIn this paper, the authors present a methodology for building realistic synthetic test cases to model combined electric and natural gas infrastructure networks. Because these networks are synthetic, that is, fictitious, they are able to be freely shared; hence, they serve to support research and development in the area of coupled‐infrastructure analysis with large‐scale realistic test cases that do not contain non‐public information. We anchor our process for building these synthetic networks in a structural characterisation of actual electric and natural gas networks. Supply and demand nodes are geographically placed, based on a combination of publicly available information from several sources and a clustering‐based method to match the right fraction of each node type, taking into account the intersection points between the electric and gas grids. Then the networks are connected with pipelines and transmission lines using a systematic graph construction method, validated against network properties including degree distribution, clustering, graph diameter, and degree of planarity, along with operational validation to ensure the simulated solution is realistic. The methodology is demonstrated by creating and validating a test case with 6717 electric buses and 2451 gas nodes.
Stegocrypt: A robust tri‐stage spatial steganography algorithm using TLM encryption and DNA coding for securing digital images
Authors: Alexan Wassim , Mamdouh Eyad , Aboshousha Amr , Alsahafi Youssef S. , Gabr Mohamed , Hosny Khalid M. 
Published: September 2024

AbstractThis research work presents a novel secured spatial steganography algorithm consisting of three stages. In the first stage, a secret message is divided into three parts, each is encrypted using a tan logistic map encryption key with a unique seed value. In the second stage, the encrypted parts are transformed into quick response codes, serving as a layer of channel coding. Subsequently, the quick response codes are decoded back into bit‐streams. To enhance security, a uniquely‐seeded Mersenne Twister key is generated and employed to apply DNA coding onto each bit‐stream. The resulting bit‐streams are then embedded in the least significant bits of the RGB channels of a cover image. Finally, the RGB channels are merged to form a single stego image. A comprehensive set of experimental analyses is conducted to evaluate the performance of the proposed secure steganography algorithm. The experimental results demonstrate the algorithm's robustness against various attacks and its ability to achieve high embedding capacity while maintaining imperceptibility. The proposed algorithm offers a promising solution for secure information hiding in the spatial domain, with potential applications in areas such as data transmission, digital forensics, and covert communication.